A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications

Background The University of Southampton, in collaboration with the University Hospital Southampton (UHS) NHS Foundation Trust and industrial partners, has been at the forefront of developing three-dimensional (3D) imaging workflows using X-ray microfocus computed tomography (μCT) -based technology. This article presents the outcomes of these endeavours and highlights the distinctive characteristics of a μCT facility tailored explicitly for 3D X-ray Histology, with a primary focus on applications in biomedical research and preclinical and clinical studies. Methods The UHS houses a unique 3D X-ray Histology (XRH) facility, offering a range of services to national and international clients. The facility employs specialised μCT equipment explicitly designed for histology applications, allowing whole-block XRH imaging of formalin-fixed and paraffin-embedded tissue specimens. It also enables correlative imaging by combining μCT imaging with other microscopy techniques, such as immunohistochemistry (IHC) and serial block-face scanning electron microscopy, as well as data visualisation, image quantification, and bespoke analysis. Results Over the past seven years, the XRH facility has successfully completed over 120 projects in collaboration with researchers from 60 affiliations, resulting in numerous published manuscripts and conference proceedings. The facility has streamlined the μCT imaging process, improving productivity and enabling efficient acquisition of 3D datasets. Discussion & Conclusions The 3D X-ray Histology (XRH) facility at UHS is a pioneering platform in the field of histology and biomedical imaging. To the best of our knowledge, it stands out as the world's first dedicated XRH facility, encompassing every aspect of the imaging process, from user support to data generation, analysis, training, archiving, and metadata generation. This article serves as a comprehensive guide for establishing similar XRH facilities, covering key aspects of facility setup and operation. Researchers and institutions interested in developing state-of-the-art histology and imaging facilities can utilise this resource to explore new frontiers in their research and discoveries.

range of services to national and international clients.The facility employs specialised μCT equipment explicitly designed for histology applications, allowing whole-block XRH imaging of formalin-fixed and paraffin-embedded tissue specimens.It also enables correlative imaging by combining μCT imaging with other microscopy techniques, such as immunohistochemistry (IHC) and serial block-face scanning electron microscopy, as well as data visualisation, image quantification, and bespoke analysis.

Results
Over the past seven years, the XRH facility has successfully completed over 120 projects in collaboration with researchers from 60 affiliations, resulting in numerous published manuscripts and conference proceedings.The facility has streamlined the μCT imaging process, improving productivity and enabling efficient acquisition of 3D datasets.

Introduction
3D X-ray Histology (XRH) is a microfocus computed tomography (microCT/μCT)-based technology that allows nondestructive, high-resolution three-dimensional (volumetric) imaging of tissue specimens for histology, with applications in the fields of biomedical research, pre-clinical investigations, and clinical histopathology.The term extends its applicability to encompass complementary X-ray μCT modalities, such as synchrotron-based μCT, phase-contrast imaging, photoncounting imaging, and more, reflecting its broader scope.In the context of this paper, 3D XRH represents a pioneering attenuation μCT -based technology developed as a result of the proof-of-principle study published in 2015 1 .In that study, we demonstrated that soft tissue samples routinely prepared for light microscopy-based conventional histology can be imaged using standard (attenuation-based) X-ray μCT.μCT achieves sufficient soft tissue contrast between the fixed soft tissue and the paraffin wax embedding medium for imaging the tissue microstructure in three dimensions (3D).This has been achieved by carefully selecting, adapting, and re-designing X-ray imaging hardware and optimising imaging protocols.With a spatial resolution at the μm scale, XRH offers detailed visualisation of microstructural features and supports correlative imaging workflows between other histology modalities across the scales such as 2D light microscopy-based histology 2 , immunohistochemistry 3 , but also electron microscopy 4 .Importantly, it also enables imaging of routinely prepared tissue blocks, providing a comprehensive view of the specimen before any sectioning occurs.XRH provides various visualisation modes akin to those used in clinical radiology, including multi-planar reconstruction (MPR), maximum intensity projections (MIP), interactive volume rendering, and 'on-the-fly' arbitrary slicing 2 .Beyond its imaging capabilities, XRH encompasses a complete and continually evolving workflow that ensures reliable sample traceability, enhances correlative imaging visualisation, supports data sharing and reporting, and automates metadata parsing.This makes it a valuable tool for advancing our understanding of tissue microstructure in the field of histology research and diagnostics.XRH 2,5 offers a previously inaccessible view of soft tissue and its microanatomy for biomedical research, providing information about spatial heterogeneity 6 , interconnectivity of components 7 and spatial growth (e.g.vascular invasion 8 , tumour margin determination 9 ), which are not always accessible through 2D imaging such as slide-based microscopy for standard histology.Due to its spatial resolution at a microscopic scale, XRH is also well suited for microanatomic studies in preclinical and clinical applications across a wide range of disciplines, including pulmonology, urology, oncology, cardiology, orthopaedics, and neurology 10 , as it is non-disruptive to the current histology workflows (see Figure 1).It is noted here that in this "introduction", we do not provide a comprehensive literature review Figure 1.XRH integration into existing histology workflows.Non-destructive 3D XRH imaging can be seamlessly integrated into the protocols used for conventional 2D histology and enhance them by providing high-resolution 3D data.The XRH data can also be used to optimise physical sectioning of the tissue block for downstream conventional histology by identifying the areas of interest within the block and slicing accordingly.

Amendments from Version 1
This version incorporates feedback from four reviewers aimed to enhance clarity, accuracy, and overall completeness of our work.The novelty elements of our work have been emphasised and outlined in a dedicated section.Clarification of the context under which the term "3D X-ray Histology" (XRH) is used in this work is provided, and the uniqueness and novelty of our approach are explicitly defined within the paper's scope.The field of μCT -based histology is now presented with greater depth via the incorporation of additional references and review papers, through which readers can gain a more comprehensive overview.Advantages and disadvantages of the presented technology faced by users of the XRH facility and its staff are outlined and discussed, including imaging trade-offs, data size-, processingand standardisation challenges.An extensive section discussing the non-destructive / non-intrusive nature of the technology has been included.This addition addresses concerns about potential damage to the tissue from ionising radiation during XRH imaging and damage occurring during tissue preparation prior to XRH imaging.Furthermore, all the X-ray source characterisation data for the μCT systems are included into the updated supplementary materials file and are made available via zenodo.org.Finally, an additional affiliation is added for all authors affiliated with the University of Southampton, Institute for Life Sciences (IfLS).
Any further responses from the reviewers can be found at the end of the article of all X-ray μCT-based techniques used and/or developed to date for studying soft tissue.Instead, we introduce the inception of our 3D X-ray Histology journey, along with the XRH facility and its rationale.Numerous colleagues have done extensive work in this field, and we refer readers to explore the domain through articles like [10][11][12][13][14][15][16][17][18][19] for a more comprehensive overview.The first complete system for 3D XRH using convectional (attenuation-based) μCT was developed by our team as part of a Wellcome Trust Pathfinder project (Development of microcomputed tomography (μCT) for enhanced diagnosis and prognosis in interstitial lung diseases (ILD), WT109682MA; 2016-17) in close collaboration with Nikon X-Tek Systems Ltd.The Med-X (prototype) 3D XRH system was optimised for soft tissue imaging in a biomedical research/clinical environment, deploying strategies to allow for a smaller footprint and lowering manufacturing cost 2 ; also see section 4.1.That system was commissioned in August 2016 and is installed in the Biomedical Imaging Unit (BIU) at the University Hospital Southampton (UHS) NHS Foundation Trust.
Follow-up funding from Wellcome Trust ('Foundations for routine 3D X-ray histology', 212940/Z/18/Z; 2019-23) allowed us to further develop a complete framework for non-destructive 3D (volume) imaging and analysis of standard formalin-fixed and paraffin-embedded (FFPE) tissue samples using μCT.This includes standardisation and automation of image acquisition and processing workflows, exploring new X-ray imaging hardware, and setting up a data and sample management infrastructure (software and hardware) that meets the tracking and reporting needs of a clinical workflow and easily links together images of the same tissue taken using different modalities.By using the experience gained with the Med-X system and other engineering scanners, two new anodes for the X-ray source, expanding the capabilities for both higher-resolution and higher throughput soft tissue imaging, have been designed, built and characterised.The data and sample management infrastructure has been the cornerstone of a new facility designed specifically for XRH (www.xrayhistology.org),currently operating at the University Hospital Southampton (UHS) site.The XRH facility supports 3D histology imaging studies and provides services compatible with current clinical histology and research workflows.As it is jointly run by the μ-VIS X-ray Imaging Centre (www.muvis.org)and the Biomedical Imaging Unit (www.southampton.ac.uk/biu/), it brings together expertise in engineering and X-ray imaging with biomedical imaging, electron microscopy and histology.
In this paper, we present the 3D XRH facility in Southampton and how it meets the needs of the scientific community for biomedical research and (pre)clinical applications.We provide details on the specifications of the machines, explain how image data are acquired and processed and discuss general considerations relevant to setting up an XRH facility.As use cases of the technology, we put forward biomedical research and preclinical applications undertaken in different fields, and we outline our plans for future developments of the technology.

3D XRH facility design: Meeting community needs
During the planning phase of the XRH facility, we reached out to our national (UK) and international network of collaborators and potential users of the XRH facility (51 biomedical and 15 non-biomedical).Using the questionnaire shown in the Supplementary Materials, we collected feedback about the aspects of the 3D XRH technology that would be most beneficial to their work and what new opportunities they could foresee accessing by exploiting XRH. 30 community members responded (response rate: 45%).The participants were working in the following fields: biofilms, cancer, cardiovascular, cell biology, developmental biology, liver, lymphatic system, musculoskeletal, neuroscience, respiratory, and tissue engineering research (Figure 2a).Approximately 80% of the participants responded that imaging the microstructure in 3D using isotropic volume image elements (voxels) is highly important for their work.High image contrast from unstained tissues and high-throughput data processing and handling workflows were also highlighted as being of high importance by approximately half of the participants.Interestingly, high-throughput data processing and/or analysis seemed to rank higher in importance compared to high-throughput image acquisition, emphasising data processing/analysis as a critical aspect for the uptake of the technology.Other less popular aspects included the potential for correlative imaging between XRH and conventional histology, data compatibility with picture archiving and communication systems (PACS), and the DICOM digital image format (Figure 2b).The community recognised that XRH raises meaningful opportunities for several applications.Almost 50% of the responders identified significant opportunities in image-based mathematical modelling and 3D histology for pathology applications and research.These opportunities were followed by applications in correlative imaging (between 3D XRH and conventional 2D histology data) and assessment of 3D structural element connectivity (Figure 2c).
The results from this survey provided the rationale for the design of the XRH facility, starting from sample preparation up to processed outputs.A streamlined framework for the facility was needed to enable rapid sample throughput and image processing of high-resolution data without sacrificing the quality and resolution of 3D image acquisition (see Figure 1).Therefore, in addition to state-of-the-art hardware, software, and imaging protocols run by specialist experts, the IT infrastructure is a crucial aspect of the facility.It streamlines this workflow and makes data tracking future-proof.Below, we describe these key aspects of the facility and explain specific features included in the design to meet the community's needs.

Microfocus tomography (μCT) imaging
In keeping with medical CT, μCT imaging is accomplished by placing the sample in the X-ray beam path and capturing projected X-ray absorption patterns (radiographs) over a large number of equidistant rotation angles (typically hundreds to thousands).In medical CT, high-resolution peripheral quantitative computed tomography (HR-pQCT) of extremities and the in vivo small animal scanners, the X-ray source and the detector rotate in a gantry system around the patient or the animal.In the laboratory μCT systems, however, the specimens (typically a few mm to cm in size) are scanned ex vivo with the X-ray source and detector fixed in space while the sample rotates on a rotating stage (Figure 3).On completion of a scan, CT-specific algorithms are used to derive (reconstruct) the sample's internal structure.These algorithms use mathematical tools that combine the attenuation information captured on the radiographs to reconstruct the internal microstructure of the object by mapping local variability in attenuation properties, depended upon a number of factors including material-and electron-density and scattering and diffraction properties 20 .These are then reconstructed into a 3D rectangular grid of cuboidal building blocks known as voxels.The principles of medical and μCT imaging and the various acquisition modes are comprehensively reviewed in 14,20.
Both medical CT and conventional μCT use X-ray absorption (attenuation) for contrast.Here, we are focusing on the absorption-based technology.Still, for completeness, it is worth noting that X-rays also behave as waves (wave-particle duality), altering their wave properties like phase when interacting with matter.Phase alterations can be turned into intensity variations in the image (phase-contrast μCT) using specialised hardware and appropriate reconstruction algorithms 21,22 .Phase imaging offers enhanced contrast compared to absorption techniques and has been used to visualise components of soft tissues, including kidney 23 , peripheral airways 24 , pulmonary and bronchial arteries 25 , and muscle 26 , it has therefore been identified as a promising approach for virtual histology 27,28 .However, most phase-contrast imaging techniques still rely on monochromatic or very narrow-band X-ray spectra accessible only in synchrotron radiation sources, which are few and far between.At the same time, laboratorybased phase-contrast imaging is gaining ground, with groups demonstrating the great potential of this approach in a more accessible setup.Limitations still remain, such as very long scan duration (>24h) 28 , small field of view, low resolution 29 or complicated experimental setup with costly, X-ray-absorbing optical elements 22 , which render these techniques impractical for use in a clinical setting.Nevertheless, phase-contrast images offer significant added value to conventional (absorptionbased) μCT.Some of these drawbacks are slowly being addressed for specific histology applications, such as involved margins in breast specimens 13 .
Depending on the image acquisition mode, reconstruction and/ or processing method, voxels in 3D μCT images can be isotropic (equal size edge in x-, y-, and z-direction; x = y = z) or anisotropic (x ≠ y ≠ z ).Unless otherwise stated, voxels are considered isotropic by default, meeting the community requirements for truly 3D imaging (Figure 2-B).Similar to key anatomical plane views in medical CT (axial, coronal, sagittal), the μCT data can also be viewed as a series of 2D slices, allowing data visualisation from different viewpoints.

μCT systems at the 3D XRH facility
The μCT scanners of the 3D XRH facility at the University of Southampton (Figure 4) are built using off-the-shelf hardware, such as commercially available flat-panel Perkin Elmer X-ray detectors, standard X-ray CT cabinets (XT H 225 series; Nikon X-Tek Systems Ltd, Tring, UK), and open-tube X-ray sources.The tube design results in a lower cost of ownership and increased flexibility in target assemblies.These standard parts have then been adapted to better suit soft tissue imaging.
Med-X prototype system.The Med-X prototype system (Figure 4) is the first commercial μCT scanner optimised for imaging standard FFPE specimens.These unstained soft-tissue specimens fit seamlessly into biomedical and clinical histopathology workflows.The system was specified on the back of the successful work 1,7 conducted using a much larger walk-in experimental chamber, and the design was optimised for use in a medical/clinical environment.On this account, a lower energy X-ray source was used, allowing for a smaller footprint, lower weight, and reduced manufacturing cost.The system was built as part of a Wellcome Trust Pathfinder project in collaboration with Nikon X-Tek Systems Ltd and was commissioned in August 2016 at the Figure 3. Schematic of a benchtop μCT scanner.The X-ray source on the left uses a beam of electrons fired at a metal target to produce a cone of X-rays.This conical X-ray beam then passes through the sample to the detector.The sample rotates on a rotation stage and for each angular step, a different X-ray radiography or projection image is taken and sent to the reconstruction computer for further processing (also see Supplementary Video 4).
University Hospital Southampton.3DHISTECH Ltd has since acquired the technology and it is furthered developed into a commercially available scanner for histology applications under the name PANNORAMIC-X®.
The Med-X prototype is equipped with a 130 kVp/225 W multi-metal (W, Mo, Cu, Ag) microfocus reflection X-ray source with a focal spot size down to 2.5 μm, which allows μCT imaging at spatial resolutions down to approximately 5 μm (see extended data).Note that for the Med-X system at a magnification factor of 136x, the nominal minimum voxel size is 1.46 μm.However, what's critical is the source's spot size, which characterises the equipment and controls the maximum achievable spatial resolution.We've characterised all mentioned spot sizes using plain radiography and high-resolution phantoms such as Jima stars.Spatial resolution is conservatively assumed to be no better than 2x the spot size, as confirmed with a touching spheres phantom.The X-ray imaging detector is a 2000 × 2000 pixels panel with an active area of ca.400 × 400 mm 2 , which allows imaging specimens with an effective diameter less than 40 cm.Using this system at the 3D XRH facility, a range of soft tissues have been imaged to date, including healthy and fibrotic human lung FFPE samples 2 , lymph node biopsies 29 , human pulmonary lymphatics 30 , placenta 31 , HSC-3 cell-xenografted severe combined immunodeficiency (SCID) mouse tissue 32 , bone marrow biopsies, as well as hard tissues 33 and micro-implants in situ such as cochlear implants 34 .

Customised XRH XT H 225 ST system.
The XRH XT H 225 ST scanner (Figure 4) is a customised μCT scanner based on an XT H 225 ST system (Nikon X-Tek Systems Ltd, Tring, UK).It is a next-generation XRH scanner for biomedical research, preclinical and clinical studies.Like the Med-X prototype, it has been custom-designed in house and optimised for imaging standard FFPE unstained soft tissue specimens, but sample throughput has been improved for clinically relevant turnaround times and scanner versatility increased as outlined below.
Specifically, the versatility has been increased by introducing an X-ray source (Nikon X-Tek Systems Ltd, Tring, UK) with interchangeable X-ray target heads, a bigger cabinet, and a dual detector geometry.X-ray generation can be achieved using any of the following targets: (1) For standard usage: a 225 kVp/225 W multi-metal (tungsten (W), molybdenum (Mo), Copper (Cu), Silver (Ag) reflection target with a 2.5 μm focal spot size up to a power of 7 W (2) For high throughput: a custom 130 kVp/130 W molybdenum rotating target with a 10 μm focal spot size up to a power of 50 W (3) For high resolution: custom 180 kVp/20 W molybdenum transmission target with a 1.5 μm focal spot size up to a power of 5 W Molybdenum has been shown to give better contrast when imaging at energies at or below 100kV, due to the "softer" X-ray spectrum produced; that is a spectrum exhibiting a lower mean energy compared to that of tungsten.This is mainly due to the large contribution of the lower energy K-shell peaks at 17 keV & 19 keV (compared to 60 keV & 67 keV for tungsten), which contribute approximately 30% to the total beam intensity at an acceleration voltage of 100 kVp 35,36 .Previously a molybdenum target has only been available as part of the multimetal reflection target (1), which limits both the maximum power available and minimum focal spot size available.For the reflection target the maximum energy/power combination is only practical when using the tungsten option, on all other metals using this combination will result in the target melting.The integration of the innovative molybdenum rotating target, a pioneering feature of this facility, offers significant advantages over static reflection targets.This advancement allows for enhanced power generation and a smaller spot size, resulting in improved scanning capabilities.
Particularly for applications that require high throughput at a spatial resolution lower than 20 μm, along with excellent soft tissue contrast, this technological enhancement proves to be highly beneficial 37 .The rotation of the target wheel in a rotating target exposes different portions of the target material to the electron beam over time, resulting in improved heat dissipation.This allows the electron beam to maintain its optimal focus for higher X-ray power setting compared to a reflection target.The process of spot-defocusing, which spreads the heat load onto a larger area and safeguards the target from melting, also initiates at 14 W instead of 7 W. Furthermore, the defocusing curve is less steep compared to that of a reflection target, enabling the source to operate at a spot size smaller than 100 μm at 100 W. In practical terms, for scans with resolution requirements <10 μm this leads to shorter scan times for the same resolution or an increase in resolution by over a factor of 2x when using X-ray power exceeding 30W; that is a spatial resolution performance at 30 W of 45 μm and 90 μm for the rotated and the reflection target respectively.The molybdenum transmission target (3) is also a first of its kind target enabling higher resolution scans than has previously been possible using a molybdenum target in a Nikon lab-based CT system.This increased resolution comes at the cost of lower power, which is further compounded by the fact that the geometric magnification required to achieve this resolution means the detector has to be further away from the source, which further reduced the flux available per pixel due to the inverse square law.These additional targets expand the capabilities of the facility in ways not previously possible.
X-ray detection for volumetric imaging (μCT) or planar imaging (radiography) can be achieved with a 2850 × 2850 active pixel high-sensitivity flat panel detector, with an active area of 432 × 432 mm 2 .The detector is mounted on a motorised gantry allowing additional flexibility with regards to source-to-detector distance, which in turn effects acquisition time through fluxmodulation.The cabinet has also been adapted to accept a SANTIS 3204 HR photon-counting detector manufactured by DECTRIS 38 for noise-free (no readout noise and dark current) and energy-sensitive X-ray detection.This detector has a resolution of 4150 × 514 pixels and allows for direct detection of X-rays rather than needing a scintillator and optical detection system 39,40 .The use of direct photon-counting techniques within XRH is currently being investigated.
The XRH XT H 225 ST scanner is equipped with a sample exchange autoloader (Figure 5 & video 41 ) which allows the system to automatically process a batch of samples.The autoloader system can either take up to 10 samples as a combination of large-format and standard histology cassettes.It can also support a rack of 14 standard cassettes.The time taken to exchange the samples allows the automatic reconstruction system to process scans in the batch while the batch is progressing.These features combined enable the scanner to run continuously overnight and over several-days, dramatically increasing the sample throughput available.The later directly impacts the cost of imaging, significantly reducing the cost per scan by making a more effective use of the otherwise idling time over-night and by reducing operators' time.For example, a batch scan of multiple 2h-long scans without the autoloader would require operators' intervention every two hours and will result in a throughout of up to 5 samples per (working) day, when the autoloader requires an upfront operator's intervention to program the batch (1-2 hours) and resulting throughput increases to 11 samples by utilising overnight imaging.The cost-per-scan then decreases by a factor of ~ 2.5 when the autoloader is used.
This key feature has enabled an increase in throughput of XRH samples and enabled the lab to continue to operate during the COVID-19 pandemic scanning throughout the week but only requiring access one day a week to setup the autoloader with multiple scans.
The autoloader system eliminates the need for a separate robot system to exchange samples on the scanner manipulator.Instead, it utilizes the existing manipulator by replacing the flat plate of the rotate stage with a tapered rod.This tapered rod fits into a matching receptacle in the autoloader sample holders, which are located in milled recesses within slots of a rack.The rack is equipped with micro-switches to detect collisions, pausing the process when a collision occurs until an operator intervenes.When not in use, as is the case when the SANTIS is fitted, the rack mounting system can be removed or fitted with blanks that satisfy the interlocked micro-switches.The sample holders are 3D printed, initially using an SLS printer and later developed in-house using FDM and resin printers for prototyping and high accuracy printing respectively.The flexibility of having both printers on-site enables rapid design, testing, and production of new mounts.The software control of the autoloader system integrates with the scanner's Inspect-X control system through inter-process communication (IPC).
The setup involves determining scan parameters and archiving a protocol in Inspect-X.Samples are affixed to the holders using hot-melt glue or cyanoacrylate, allowing multiple scans per sample with the option to change parameters between scans.Changing the target metal requires a physical adjustment, meaning all scans on a tray must use the same target material.Trays can be configured on a separate computer and started later.The IPC also facilitates a utility for on-demand pickup or return of individual samples.
The improvements to the X-ray scanning hardware that have been integrated into both the Med-X and XRH scanners including customised X-ray anodes, auto-loader and ability to mount a DECTRIS SANTIS 3204HR, enrich the user experience by providing a singular, comprehensive suite of cutting-edge equipment specifically tunned for soft-tissue imaging available at a single facility.This resource empowers users with an expanded and diverse range of acquisition modes to choose from.
Innovative Hardware Advancements.The substantial portion of this project has been dedicated to improving hardware components.In collaboration with our industrial partner, we have focused on optimising our systems with a primary objective of achieving the highest possible image quality when imaging standard soft-tissue FFPE tissue cassette specimens.These advancements have been designed with practical clinical applications in mind.We have focused on several key modifications to our system including: • Adjusting the electron-beam angle of incidence onto the target to mitigate the heel-effect, which, if unaddressed, results in an uneven mean energy distribution of the X-ray beam from the bottom to the top of the illumination field.This adjustment is crucial for precise quantitative imaging and ensuring consistent tissue visualisation across the entire illuminated area.
• Modifying the X-ray source housing to diminish the generation of secondary X-ray sources arising from interactions between back-scattered electrons and the target housing.Although this occurrence poses minimal concerns when imaging materials with high attenuation contrast, it presented challenges when imaging unstained FFPE specimens.These specimens require us to operate within extremely narrow contrast windows within which "phantom" images of the specimens, stemming from the secondary sources, became noticeable.
• Co-designing the Med-X prototype enclosure to reduce its footprint and its weight by lowering the system's maximum energy to 130 kVp using a soft-limited 160 kVp source.We also made adjustments to its internal components by adding cover-panels, to facilitate hassle-free cleaning and diminishing the potential for biohazardous contaminations in clinical settings.
• Modifying the standard tungsten rotating target to a molybdenum rotating target, to allow for higher flux soft X-ray imaging and improving throughput within more clinically relevant time-frames.This was paired with the development and deployment of our XRH XT H 225 ST system of our automatic sample exchanger.This upgrade was complemented by the development and implementation of our automatic sample exchanger in the XRH XT H 225 ST system.
• Specifically designing the sample racks for the autoloader system.With that we have ensured their adaptability to accommodate either the maximum number of standard 30 mm histology cassettes or to offer 10 slots capable of holding either a standard 30 mm cassette or 60 mm giga-cassettes.
• Modifying the standard tungsten transmission target to a molybdenum transmission target to allow imaging with softer X-rays at much higher resolution.

Data visualisation and processing
Following μCT imaging, several visualisation options are made available to the users as part of the standard image post processing workflows at the 3D X-ray facility.As in radiology, μCT images and hence XRH data can be presented to the viewer using a range of 2D and 3D visualisation modes which include, but are not limited to, multi-planar reconstruction, maximum intensity projections, or interactive volume rendering.Depending on the nature of the visualisation, the renderings are offered as single volume files (including: .raw,.tif,.DICOM), which can be opened with any available 2D/3D image viewer (e.g., Fiji/ImageJ 42 ) and complemented with video files when motion or scrolling is important for 3D data interpretation.These video files are significantly smaller than the reconstructed volume and can be easily and quickly sent to collaborators as part of an initial report about the scan.This allows users to review their data with minimal effort and without requiring prior experience with visualisation software.The generation of these videos has been semi-automated by the creation of a toolbox of Fiji macros, which is made available to readers through Zenodo.org(https://doi.org/10.5281/zenodo.8253271).

2D visualisation.
A single slice is a cross-sectional (2D) view of the 3D image stack along a selected plane.XRH data sets are normally oriented (resliced) in a way that scrolling through the stack along the xy-plane emulates the physical histology slicing of the tissue block.This way, every xy-slice through the image stack (blue plane in Figure 6) is parallel to the histology cassette (where applicable) and the xy-slice scrolling runs from the wax block's surface towards the cassette (Supplementary Video 1), similarly to action of the microtome knife during physical sectioning in standard histology workflows.Orthogonal xy-, xz-, and yz-planes (blue, red, and green planes in Figure 6, Supplementary Video 2, and Supplementary Video 3) are also referred to as ortho-planes, which can be visualised in parallel for multiplanar viewing (see Figure 6), similar to visualisation of medical CT or magnetic resonance imaging (MRI) data.X-ray μCT slices are single channel images, which usually represent the intensity value of the captured signal (X-ray attenuation).Although μCT slices are greyscale, pseudo colouring can be applied through look-up tables (LUT), where each pixel's grey value is associated with a colour.This can be used to generate photorealistic renderings for communicating the data with a wider audience, or to "digitally stain" the μCT image to emulate conventional histology outputs 9 .

2.5D (thick slice modes) visualisation.
Thick-slice viewing is a viewing mode that allows rendering of the voxel intensity of multiple consecutive slices within a 3D data set onto a single slice, based on specific criteria or operations (Figure 7).A thick-slice image represents the result of these criteria or operations.For instance, a thick-slice view can be generated by applying one of the following projection methods across a pre-selected number of slices (n), which defines the slice thickness: (1) Maximum intensity projection (MIP): A MIP analyses each voxel at a specific position (X, Y) within the xy-plane, for n slices (e.g.(X,Y,1), (X,Y,2), ... , (X,Y,n)) along the z-axis, and renders the voxel onto the viewing plane, which exhibits the maximum value (Figure 7 and Supplementary Video 5).
(2) Average intensity projection (AVG): an AVG analyses each voxel at a specific position (X, Y) within the xy-plane across n slices (e.g.(X,Y,1), (X,Y,2), ... , (X,Y,n)) along the z-axis and renders the average value of those voxels onto the viewing plane (Figure 7 and Supplementary Video 6).
(3) Sum intensity projection (SUM): SUM analyses each voxel at a specific position (X, Y) within the xyplane across n slices (e.g.(X,Y,1), (X,Y,2), ... , (X,Y,n)) along the z-axis and renders the sum of the value of those voxels onto the viewing plane (Figure 7 and Supplementary Video 7).
Thick-slice modes are particularly useful when looking at large anatomical structures that extend outside the 2D plane.By creating thicker slices, the observer can get a better view of the overall structure which is particularly useful in visualisation the development of vessels and airways, tumour infiltration, emphysema, microcalcifications or thrombi.
Thick-slice rolling is a 2D thick-slice viewing that allows rolling of a pre-selected number of slices (n) along the z-axis of the 3D data.A single thick-slice roll forwards is accomplished by translating the thick-slice by one single slice forwards; that is moving forward by one (+1) slice from the first and n th element and reapplying the criteria or operations to the new slice As with single thick-slices, "thick-slice rolling" allows insights into the 3D shape of complex structures, that are difficult to be interpreted in 2D, and they do so in a dynamic way allowing the viewer to scroll in and out of plane and follow the development of that structure in the volume.Examples of these modes can be seen in Video 5, Video 6 and Video 7.

3D (volume) visualisation.
Volumetric rendering is a 3D visualisation mode used for displaying the 3D grid of voxels contained in 3D data sets such as XRH data sets (Figure 8).Volume rendering algorithms visualise images of volumetric data sets by mapping data values to opacity and colour, without explicitly extracting geometric surfaces of the object.Each voxel corresponds to a location in data space and has one or more data values associated with it.In the case of a CT reconstruction, volumetric rendering visualises the 3D map of appropriately scaled X-ray attenuation values or more precisely, linear X-ray attenuation coefficients.Roughly speaking, these values represent the density distribution within the sample, which in turn provides information about the microanatomy of the tissue in 3D.Whole-sample visualisation allows for a multi-perspective examination of the tissue, minimizing the risk of inadequate tissue sampling associated with destructive conventional histology, where slides are cut once by a microtome in a pre-defined plane.This has been tested by a team at the Department of Pathology at the Memorial Sloan Kettering Cancer (New York, USA), one of the very early adopters of XRH, where XRH-guided intraoperative cryo-sectioning was used on the sites of lesions and surgical margins 9 .In line with the 2D thick-slice renderings, 3D MIP or AVG rotation can also be generated.In this mode, the dataset is rotated about a userdefined axis, and the rotation allows for depth and perspective interpretation of the brightest features as demonstrated previously (see Supplemental Video in 6).3D renderings are also used to visualize novel quantitative features extracted from the volumetric data such the local 3D thickness 6 (see also Figure 8).

Image processing and quantitative analysis.
One of the primary needs for users of the 3D XRH facility is efficient data processing and handling workflows (as outlined previously), including the quick extraction of data from generated images.
To address this, the XRH team offer their expertise in a variety of software suites available on-site for processing and analysing μCT datasets.The software options range from open-source suites, such as Fiji/ImageJ 42 , ITK Snap 43 or HOROS® (The Horos Project) to commercial software suites such as A bottleneck in speed of data extraction is often in the segmentation, or digital isolation, of specific features in the μCT data set.Segmentation is necessary to undertake quantitative analysis of these features.To segment an image through automated processes, high image quality is vital.On this account, μCT scanning protocols have been optimised for soft tissues over the last few years, in terms of signal-to-noise ratio, image contrast and other image quality factors.Moreover, image post processing routines have been streamlined including processing scripts, for tasks such as image enhancement, normalisation, or artefact reduction that can be run automatically on open-source software such as Fiji/ImageJ.This is offered to users, permitting high-throughput comparative analysis on multiple μCT datasets, which was another key requirement to be met for the 3D X-ray histology facility.Case study 1, presented below, is a demonstration of the use of imaging processing tools and the quantitative nature of the μCT data.

Remote visualisation.
There are multiple ways of providing remote visualisation: using display forwarding techniques such as Remote Desktop which is discussed further in this section, as well as web based viewing options which are discussed in Section 8. Options for sharing the data are discussed a later section.During the COVID-19 pandemic remote visualization has proven valuable for internal users at the University of Southampton as this it provided access to the computational power and storage needed to process the data.This was limited to internal users because opening the infrastructure wider would require consideration of: different data policy models, cybersecurity measures for data protection, establishing user accounts and institutional policy adherence, managing licensing issues for commercial software, and safeguarding intellectual property.By carefully evaluating these factors, remote visualization can be effectively implemented for first-time users, offering them significant advantages while ensuring data security, policy compliance, and addressing licensing and IP concerns.Different methods for sharing the datasets currently in use are discussed in section 6.2, and future options to be investigated are covered in section 8.

Case study 1: Quantitative imaging for cancer research/ diagnosis.
In this study 32 , XRH was used to assess the synergistic anti-tumour potency of novel drug formulations.XRH was employed to provide high-resolution, 3D (volumetric) microanatomical information of tumour response to treatment by analysing the phenotype of the excised tumours (Figure 9).XRH imaging of the excised tumours allowed us to quantitatively analyse the volumetric characteristics of anatomical features, such as total tumour volume, its necrotic core, and calcifications of tumours treated with different formulations.Measuring the ratio between the total tumour volume and the necrotic tissue volume highlighted the efficacy of the Doxorubicin and Curcumin (DOX+CUR) peptide hydrogels in enhancing tumour necrosis and suppressing cell proliferation.In summary, we could show that XRH is a powerful tool for providing quantitative high-resolution anatomical information of tumour biopsies after intra tumoral administration of chemotherapeutic formulations 32 .

Bespoke data visualisation: Correlative imaging and imagebased mathematical modelling.
Bespoke visualisation options, such as correlative imaging renderings or modelling-based visualisation are important to interrogate XRH data in terms the underlying function of the biological features studied.These are application-specific modes that need to be optimised on a project-by-project basis.On our users' survey 34% of responders indicated that 2D or 3D correlative imaging could be of interest in the future and 50% were interested in image-based modelling.
Standard histological techniques such as tinctorial staining, Immunohistochemistry (IHC), or immunofluorescence enable quantitative microstructural and even biomarker specific analysis at histological resolution in 3D 2,3,44,45 .As μCT imaging is non-destructive, including tissue protein structure 9 , these protocols can be carried out on tissue that has been previously scanned using μCT.Therefore, the image data of stained tissue can be correlated to the 3D context conveyed by the μCT conducted beforehand, which enables 3D assessment of biomarker localisation through correlative imaging.If serial histological data is collected, 3D features identified by biomarker expression can also be segmented, and then assessed quantitively in a 3D context.
In addressing the challenges of registration between histology and μCT, we have encountered the issue of elastic deformation during the sectioning process and tissue slice "floating." To ensure precise alignment, we have developed an elastic registration approach, which is thoroughly described in 3 and 23.This is a versatile technique, which can work between a variety of datasets, including XRH, conventional histology, immunohistochemistry, electron microscopy or other imaging modalities.
In addition, the popularity of using μCT data sets as imaging input for image-based modelling is increasing, primarily due to the increased availability of high computational power.Input geometries for modelling can be obtained by segmenting μCT data or through correlative imaging, as discussed in the previous paragraph.As a result, XRH data can be utilized to model physiological and mechanical processes, allowing for the assessment of the impact of pathological tissue remodelling.
Example research work using these bespoke visualisation methods are described in Case study 2 and Case study 3.

Case study 2: Correlative 2D/3D visualisation and augmented tomography.
XRH can be paired with conventional 2D light microscopy-based histology and IHC.This is further supported by preliminary data demonstrating the safety of XRH against radiation damage 9 , as it does not seem to affect protein antigenicity.As such, XRH can be used for multimodal correlative imaging 2,7,8,30 , which offers a unique perspective into structure-function relationships of the studied tissues.
An example is presented here, where a human head and neck tumour specimen was imaged using both XRH and conventional histology, and the data were combined to allow for a correlative multimodal assessment of the excised specimen (Figure 10).XRH imaging was utilised to provide a 3D overview of the entire specimen, while conventional histology allowed higher resolution 2D imaging of tissue's microanatomy.The μCT and histology images shown in Figure 10 were matched to achieve accurate correspondence between the two modalities.The process involved rigid reslicing of the XRH data (rotate & translate) to accurately match the histology cutting plane and semi-automatic landmark-based registration of the 2D histology image onto the relevant XRH μCT slice as explained in 6.This way, powerful visualisations can be generated by overlaying conventional histology images on top of the wider field of view from the μCT scan (augmented tomography).This allows the higher resolution histology image to be viewed in the context of the wider tissue and structures.

Case study 3: 3D XRH-based mathematical modelling.
μCT-based XRH has shown to be an essential tool in workflows for 3D image-based modelling 30 .In this case study, we outline image processing methods to extract vascular networks within human lung tissue using correlative μCT and IHC methods.This provided 3D input geometries for successful computational fluid flow simulations between the blood and lymphatic vessel networks through parenchymal tissue.Due to altered 3D tissue microstructure, preliminary modelling results showed marked differences in lung drainage patterns at different anatomical locations within healthy lung tissue (Figure 11).This work demonstrates that high-fidelity 3D isotropic geometries produced with μCT are crucial for accurate soft-tissue image-based modelling.
IT infrastructure A high-throughput facility, such as the 3D XRH facility, requires a high-performance IT infrastructure able to support both the data volumes and data rates produced by the μCT scanners and provide management and administration features to enable efficient operation.To manage and process large datasets, it is important to carefully consider how to handle the technical complexities.The XRH facility has made their high-performance IT infrastructure more accessible to nonexperts by shielding them from some of these complexities.By adopting this approach, users are able to concentrate on the substantive aspects of their data analysis, rather than being impeded by the logistical challenges involved in data generation and management.
The new XRH XT H 225 ST scanner requires a 10 GBit/s network for acquisition of scans at the highest framerates, which is a step up compared with the previous Med-X prototype that uses a 1 GBit/s network connection.This requirement for higher network performance is due to the higher sensitivity and higher resolution detector, which can produce scans of 50 -100 GB of radiographs.This increase in network performance also had to be matched with suitable high-speed, highcapacity storage.This storage, consisting of both SSD and HDD drives, has been deployed locally to the scanner to maximise performance and reliability.The underlying hardware only forms part of the required infrastructure.To ensure that hardware problems are detected as soon as possible, a network-monitoring system is being run to provide automatic alerts of any failures or degradation in the hardware, enabling immediate action to be taken to remedy the issue.Multiple workstations are needed to ensure that data can be processed while another scan is ongoing.Data processing at the 3D XRH facility can be carried out using a combination of open-source and commercial software as described in Section 5.4.This provision is supported by graphics tablets and virtual reality headsets.Some of the samples scanned may be further (destructively) processed after scanning, which renders it impossible to reproduce scans at a later date.This means that, apart from primary storage for the scan data, systems for backing up and recovering the data in case of drive failure also needed to be put in place.

X-ray Histology Management System (XRHMS).
The μ-VIS X-ray Imaging Centre at the University of Southampton uses a custom management platform built to meet the needs of the facility 46 .When scanning high volumes of samples, it is important to record scan parameters and metadata to ensure scan reproducibility.This management platform allows scans to be tracked and metadata to be catalogued.This system along with ideas and concepts from 47,48 have been used to develop a new management system specifically designed for use within the XRH facility.Existing lab information management systems (LIMS) such as MetaLIMS 49 , Baobab 50 , and OpenLIMS 51 were investigated but these were either too specific to their intended application domain, not maintained, or too heavyweight incorporating various unnecessary features.
The bespoke X-ray Histology Management System (XRHMS) we developed is used to track projects, samples, and generated XRH data throughout their lifetime, where a summary of this is shown in Figure 12.A key feature of the XRHMS is that it tracks a sample through multiple states, for example from being scanned as a fresh sample, then being FPPE-fixed and scanned again, before being sectioned, processed, stained, and imaged with conventional section-based histology.By tracking the sample through all states of processing, the data from each process can be aggregated to implement correlative imaging experiments, thus maximising the information available from a valuable sample.The XRHMS has been designed to also track the movement of data from online hot, and warm storage to cold archival storage.Additional options to present archived data publicly such as X-NAT 52 are being investigated 53 , with other options on the roadmap for future work.Finally, the XRHMS system links to the existing μ-VIS infrastructure, enabling statistics to be generated for the facility through the μ-VIS system and facilitating data sharing between the two platforms.
Efficient delivery of 3D datasets.The large file sizes of the generated datasets pose challenges not just for storage and processing, but also for sharing with collaborators.One option is to provide a USB hard-drive with all the data on it, which can be done but has several limitations.Today, the bandwidth available between universities is such that transferring a dataset online is not unreasonable, however, appropriate tools are required.One such tool is the open-source zend.to which enables arbitrarily large files to be sent to multiple recipients, within reasonable time-frames.Even when the data has been transferred to the client who requested the scan, this is not its final destination, as it should also be made available to readers if the scan results in a publication.This can be achieved by depositing the data into institutional repositories but options for online visualisation are also being explored.Another disadvantage of exporting the data to an external hard-drive is that it forms data silos, therefore scans previously taken cannot be incorporated into new projects for further analysis.This ongoing storage of previous scans poses both technological and management/authorisation challenges.
One of the challenges is finding the best way to share the 3D data generated with remote collaborators, some of which might be unfamiliar with the technology and its outputs.This created the need for easily accessible and comprehensive reporting of the XRH data, and to address this we developed a semi-automated reporting system that is linked to XRHMS.The system generates augmented Portable Document Format (PDF) reports that contain sample information, imaging settings, still images with descriptive figure legends, and links to corresponding online videos.These videos provide 3D viewing modes of the scanned samples and are easily shareable through email or other means (see supplementary materials).These videos can move through consecutive single slices of the image stack in each of the three planes XY, XZ, YX; through thick slices of processed data (MIP, Avg, StdDev); through 3D renders; or through the raw radiographs (see supplementary videos).The exact combination and parameters for video generation are currently being evaluated.Video generation is semi-automated to ensure reproducibility of results on a project-by-project basis.If a standardised set of parameters and videos is defined, this process can be fully automated (with the option of including additional videos semi-automatically).
The report also includes links to useful resources and recommendations or commentary from the XRH team.The fully formatted report can be generated with a minimum of user input before being forwarded to collaborators.The processed datasets can also be shared with collaborators online, and support for viewing and extracting all important information from them is offered.

Client access to the 3D XRH facility and XRH project enquiry workflow
There are several pathways to access the 3D XRH facility and the services offered.One of them is through the National Research Facility for laboratory-based X ray CT (NXCT) Facility, which is the UK's National Research Facility for lab-based X-ray CT of which the University of Southampton is one of its founding members (nxct.ac.uk), or directly through an enquiry to the μ-VIS X-ray Imaging Centre (www.muvis.org).
The NXCT is currently offering a limited number of free at the point of access scan days to academic, non-profit and SME clients to carry out proof of principle studies.Larger or more complicated projects that go beyond a proof-of-principle basis need to be costed, and there are procedures in place for offering such access to both academic and industrial partners.For academic clients of the facility the cost is calculated using a Full Economic Costing (FEC) model, for industrial partners it is costed on a commercial basis.In either case the clients have exactly the same access to the facilities and the staff within, the client is welcome to come and see the facility and when developing a protocol it can be helpful to have them on site to provide real-time feedback, equally we have clients where the entire process has been carried out with them in a different country.
An XRH enquiry from a client (Figure 13) through any accessing pathways will be followed up by the XRH team, who will discuss requirements, feasibility, and deliverables with the client.Once project objectives are agreed, samples will be sent to the facility for imaging, following setup of appropriate documentation.The samples would normally arrive in the facility processed, embedded in paraffin wax, and mounted on a histology cassette, although other sample states such as frozen, wet-fixed or fresh are also acceptable.For most applications, FFPE samples are imaged on the cassette to minimise sample manipulation.The standard range of equipment allows imaging at spatial resolution down to 5 μm, although higher resolutions can be achieved with minor modifications of the hardware.However, higher resolution sacrifices a larger field of view, which enables whole-block imaging and 3D visualisation of the entire tissue.XRH has been designed to seamlessly fit into established histology workflows.Consequently, complementary conventional histology and/or IHC can also be performed following imaging at the 3D XRH facility, as in the case of correlative imaging projects, either in specialised labs of the user or on site at the Histochemistry Research Unit (www.southampton.ac.uk/hru) at the University of Southampton.All samples, data and metadata are being tracked and interlinked using the XRHMS described earlier and in 54, which is designed to orchestrate the whole entire workflow.XRHMS empowers the facility scientists to fully track the journey of all samples, their associated data and metadata and interlink these with the respective projects and people involved.This system is vital for generating the sample scan reports 55 and collating client data for release before archiving.This combination of 3D X-ray histology, histochemistry and optical and electro-magnetic imaging research facility is unavailable anywhere else, and provides a unique and powerful combination that can be used to further clients research.

Discussion & Conclusions
Our 3D X-ray Histology facility can assist biomedical research and clinical practice across many disciplines.3D XRH offers amongst others (1) micrometre resolution in 3D, which allows visualisation and quantification of tissues' microstructure in their 3D context, (2) a non-destructive approach to tissue histology that makes it suitable for correlative imaging, e.g. with specific 2D histology techniques such as histochemistry and immunocytochemistry, and (3) high image contrast for non-stained FFPE specimens.
The XRH facility is currently capable of scanning FFPE, fresh, and wet tissue, and to also do quick scans of frozen samples.These quick-frozen scans can be performed by using dry ice to provide a curtain of cold air that sinks over the sample to keep it cool.This approach is simple, inexpensive and produces good results, but it has limitations if a long scanning time is required.Dry ice can cause a condensation build up around the sample which can affect image quality, and it also 'runs out' (sublimates) after a relative short period of time; i.e. 15 -30 mins depending on the size of the reservoir.To resolve this issue and extend our 'cryo'-imaging capabilities a liquid nitrogen -based system has just been purchased to allow for longer scans (> 5h) of frozen samples.This system will use a jacket of dry air around the sample which is anticipated to reduce any issues due to condensation.
Although our team's primary focus in Southampton has been on biomedical material such as clinical and animal tissues, the 3D XRH facility has been also used for samples from other sources ranging from sea corals to biofilms, and from meat substitutes for food technology advancements to pharmaceutical technology related to novel dosage forms.In the past 7 years, we have shown that it is possible to set up a facility for the μCT imaging community, with successful completion and initiation of over 120 projects involving 125+ collaborators from 60 affiliations.This has resulted in 15 published manuscripts, 10 submitted/in preparation, over 35 conference proceedings, and over 25 research funding proposals 1-3,7,23,30-32,37,44,56-59 .
The technological developments produced while setting up the 3D XRH facility improve standard μCT imaging methods by eliminating engineering complexities.Systems have been developed to support and automate processing, thus, improving productivity.Users can acquire 3D datasets quickly and easily with minimal involvement if they so wish.However, it is important to highlight that as with any imaging technique there is never a perfect imaging protocol for every application.Users' expectations must be carefully managed before undertaking scanning work.The factors that must be balanced include spatial and contrast resolution, image noise and artefact control against throughput speed.The throughput speed has the largest impact on cost from both the facility and users' point of view and can be a make-or-break point for many applications.Furthermore, in the context of μCT imaging and its correlative applications, ensuring specimen quality is of utmost importance.For example, a notable challenge in FFPE specimens of soft tissues is the presence of air bubbles resulting from suboptimal wax embedding.Strategies to address this issue include customised post-processing algorithms that effectively mitigate interference artifacts arising from the bubble-wax interfaces, or when necessary dewaxing and re-embedding.These steps ensures that, in most of the cases, the processed data is suitable for automatic thresholding, enhancing the accuracy of subsequent analyses.
Another point of consideration that should be addressed when discussing experimental planning with the client is the prospect of radiation-induced specimen damage.Such damage has the potential to influence downstream biochemical analyses, including immunohistochemistry, proteomics, and transcriptomics.Notably, existing research in this area has predominantly focused on synchrotron radiation-based studies, where substantial doses have been found to impact tertiary protein structures 60 .However, for laboratory-based radiation doses, especially within the context of the low-dose protocols typically used for soft tissue imaging in our facility (0.1 -5.0 kGy), there appears to be minimal impact on the tissue, especially concerning the affinity of tissues for histological stains 9 .Nevertheless, in the near future, we will focus on assessing protein and RNA quality to ensure the preservation of molecular integrity.This step is crucial in achieving a truly "non-destructive" technique at all hierarchical levels, which is essential for facilitating correlative biochemical analyses.
At this point, it is important to note that, while the non-destructive/non-intrusive nature of XRH imaging holds true for the X-ray imaging component of the workflow, non-destructiveness and non-intrusiveness, in the grand scheme of things, should be interpreted within the context of classical histology workflows and after the FFPE stage.Preceding stages, including sample preparation and paraffin embedding, are elements that fall outside the direct control of the workflow discussed here, as our approach is designed to primarily handle specimens that have undergone the classical sample preparation journey.However, we should highlight that classical tissue handling, including the processes leading to FFPE, can affect tissues and introduce disturbances such as tissue shrinkage.More specifically, the FFPE process induces tissue dehydration, resulting in both global and local tissue shrinkage and densification.The dehydration steps in FFPE play a pivotal role in increasing the molecular density per unit volume.This is primarily attributed to the removal of substantial quantities of hydrogen and oxygen elements from the sample, leading to a densification of the organic elements proportionate to their hydration level.This densification contributes to subtle distinctions between elements with varying degrees of hydration 61,62 .
Understanding the physical effect FFPE processing has on tissues can offer insights into the quality and interpretability of X-ray histology images.In the realm of soft-tissue imaging, particularly within the context of FFPE tissue, the majority of components consist of organic material with comparable attenuation coefficients.Interestingly, non-staining XRH protocols exploits minute X-ray attenuation variations introduced by the very process of FFPE processing.Alongside the discussed densification, formaldehyde-based fixatives also induce cross-linking of native tertiary protein structures 61,63 linking a great number of molecular subunits including those that are relatively far apart 64 .We believe that these cross-linking bonds increase localised electron density, which in turn, affects their X-ray attenuation properties.Regions with heightened crosslinking appear "denser" than their non-or less-cross-linked counterparts.Similarly, the variable densification between tissue components caused by dehydration also contributes to X-ray contrast resolution.These subtle attenuation differences are what attenuation-based XRH of FFPE specimens exploits for contrast.However, further work is needed to systematically collect data and identify the contribution of each processing stage to enhanced sample contrast and investigate the impact of common fixatives on contrast levels in histological protocols.
The impact of formalin fixation on tissue dimensions is a critical consideration in various studies, as such, it should be noted for those wanting to use our facility for absolute native tissue architecture.Formalin fixation is known to induce shrinkage in cancerous tissue, potentially leading to the underestimation of tumour staging and studies across different organs, such as the oesophagus, prostate, breast, lung, and cervix, have reported varying degrees of shrinkage following formalin fixation.For instance, oesophageal tumours have demonstrated a 10% shrinkage, while the overall shrinkage of the entire oesophageal specimen reached 50%.Prostate, breast and head and neck cancer specimens experienced an average linear shrinkage of up to 4.5%.The degree of shrinkage appears to vary among studies, suggesting that tissue constitution, including the presence of muscle or fat tissues, may influence the overall shrinkage 65 .It should be said, however, that the majority of our clients to date use relative measures and their study sample groups and controls have undergone the same level of processing.
There are also major challenges around how to distribute, annotate and archive μCT datasets.The current process of either using online transfer or shipping of hard disks is suitable for point-to-point transfer of data but has major limitations.One of the issues has been the number of different file formats produced by μCT scanners, within the μ-VIS facility we have scanners from multiple manufacturers each of which uses a different output format.This has previously been dealt with by converting into either tiff stacks or raw volumes to enable data exchange.Newer file formats such as hdf5 66 or OME-NGFF 67 as implemented by OME-ZARR 68 , may facilitate easier transfer of the raw data and associated metadata between sites, using software such as MoBie 69 .Other options for sharing the data online such as webknossos 70 , which was designed for 3DEM images, or Horos cloud (Horos, https://horosproject.org/).It is envisioned that eventually that there will be tooling and practices to enable 3D XRH data to be easily shared according to the FAIR data principles 71 .
Below we outlined some of the more-prominent challenges associated with managing clients' project at the XRH facility: • Imaging Trade-offs: Balancing the trade-off between field of view and resolution, as well as resolution to scanning time (and thus cost), poses a challenge that needs to be carefully considered.
• • Complexity of Image Analysis: Managing the intricacies of image analysis, particularly in terms of quantification, presents a significant challenge to many users, especially the ones with no or limited experience to processing of volume data.
• Need for Correlative Imaging: Correlative imaging is often required for biomarker identification, which adds an additional layer of complexity (and cost) to the workflow.

• Contrast Challenges with Non-Stained Soft Tissues:
The majority of non-stained soft tissues, when imaged in a hydrated state exhibit minimal X-ray contrast.For such specimens other approaches are much better suited, and should be considered including phase-contrast imaging 12,72 , contrast CT 19 and SRCT 17 .
• Cost of Relevant Equipment: The acquisition and maintenance costs associated with the necessary equipment capable of handling XRH data, including software and network infrastructure, can be substantial and lead to underutilisation of the data.For this reason, it is important that an XRH facility should be prepared to invest and offer access to processing infrastructure.
• Standardisation and DICOM Compatibility: Ensuring standardisation and compatibility with DICOM (Digital Imaging and Communications in Medicine) standards remains a crucial consideration, and one that can unlock clinical compatibility.
Despite of the aforementioned challenges, XRH is slowly being established as a clinical tool.The team at the Department of Pathology, Memorial Sloan Kettering Cancer Center in New York, USA, which had been one of the very early adopters of XRH, have already taken a significant step towards clinical integration by issuing a standard operating procedure (SOP) of μCT pathology 9 .We envisage this to be directly translated into a next-generation clinical image-based diagnostics and a patient stratification tool.
μCT imaging has been also utilised in a clinical context for μCT-based foetal post-mortem autopsy [73][74][75][76] .In the UK, the technique has been pioneered and it is currently offered as a service at Great Ormond Street Childrens' Hospital (GOSH), London, UK by Dr Owen Arthurs et al, who have successfully conducted more than 700 investigations to date.The technique is summarised in 73 and there is also a significant volume of published articles from the team describing the development, validation, and integration workflows [77][78][79] .
In addition, by integrating XRH as a scouting tool, one can preview and identify the optimal slicing location(s) before tissue sectioning, ensuring precise and representative sampling.This approach, which we call XRH-guided histology, holds immense potential to improve the accuracy of diagnosis, enhance patient outcomes, and revolutionize the way patient samples are analysed in the clinic (see Figure 1).
The use of non-stained FFPE specimens enables histomics, i.e., the study of tissue structure-function relationships by means of advanced microscopy techniques.On this account, by exploiting archival material stored in many hospitals and tissue banks using XRH, microstructural hallmarks of disease seen in XRH data can be fully validated in the future.This will accelerate the uptake of XRH as biomedical research tool as well as a promising technology for pre-clinical and clinical studies.
In conclusion, the 3D X-ray Histology (XRH) facility at the University Hospital Southampton (UHS) holds a truly unique position in the field, distinguishing itself as the world's only dedicated XRH facility that oversees every stage of the imaging process, from user support to data generation, analysis, user training, data archiving, and metadata generation.Its integration within a clinical environment at UHS further enhances its significance, offering unparalleled opportunities for clinical applications and collaborations.Furthermore, the facility introduces a multitude of novel aspects to the realm of histology and imaging.With state-of-the-art technology, user-friendly interfaces, and a diverse range of applications, it stands at the forefront of innovation.The facility's capacity for micrometreresolution 3D imaging enables the visualization and quantification of tissue microstructures in their full three-dimensional context, unlocking unprecedented insights, while seamlessly fits in conventional histology workflows.Its non-destructive approach to tissue histology allows for correlative imaging with specific 2D histology techniques such as histochemistry and immunocytochemistry, expanding the analytical possibilities.These aspects position the 3D XRH facility at UHS as an exceptional platform for advancing research and clinical practice in the study of tissue structure-function relationships.This article provides a comprehensive template for establishing a similar 3D X-ray Histology (XRH) facility, covering some of the key aspects.It serves as a resource for researchers and institutions interested in developing state-of-the-art facilities dedicated to advancing histology and imaging techniques.Researchers looking to take advantage of this resource or considering setting up a similar facility, are encouraged to contact and/or apply for access.With access to state-of-the-art equipment, a supportive community of experts, the new imaging facility is a valuable platform for exploring new frontiers in research and discovery.

Ethical statement
Case

Extended data
Zenodo."A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications -Supplementary Data".https://doi.org/10.5281/zenodo.10185486 80is project contains the following supplementary data: Videos: • Video 1 (A video going through the Z stack in single slices.This is a cross-sectional view of the XRH image stack along the XY plane.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue).
• Video 2 (A video going through the Y stack in single slices.This is a cross-sectional view of the XRH image stack along the XZ plane.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue).
• Video 3 (A video going through the X stack in single slices.This is a cross-sectional view of the XRH image stack along the YZ plane.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue).
• Video 4 (3D X-ray histology (XRH) is a μCT -based workflow tailored to fit seamlessly into current histology workflows in biomedical and pre-clinical research, as well as clinical histopathology.Microanatomical detail can be captured from standard (non-stained) formalin-fixed and paraffin-embedded (FFPE) tissue blocks).
• Video 5 (Average Intensity Projection (AIP) of the sample through the Histologically relevant plane.This is a 2D visualisation rendering the Average Intensity of 20x single XY slices along the z-axis of the stack.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue).
• Video 6 (Maximum Intensity Projection (MIP) of the sample through the Histologically relevant plane.This is a 2D visualisation rendering the Maximum Intensity of 20x single XY slices along the z-axis of the stack.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue).
• Video 7 (Standard deviation projection of the sample going through the histologically relevant plane.This is a 2D visualisation rendering the Standard Deviation of 20x single XY slices along the z-axis of the stack.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue).

* Videos 5 -7 (Also referred to as "thick-slice rolls" -Thickslice rolling is a 2D thick-slice viewing that allows rolling of a pre-selected number of slices (n) along the z-axis of the 3D data. A single thick-slice roll forwards is accomplished by translating the thick-slice by one single slice forwards; that is moving forward by one (+1) slice from the first and nth element and reapplying the criteria or operations to the new slice sub-stack).
The questionnaire used to collect feedback about the needs of the XRH community: • Survey.docx Exemplar report of a semi-automatically generated augmented PDF file that contain sample information, imaging settings, still images with descriptive figure legends, and links to corresponding online videos: System performance data ZIP that contain imaging data collected through different systems and setups at the XRH facility at the μ-VIS X-ray Imaging Centre at the University of Southampton for the purpose of acceptance and/or system performance characterisation.Below is an overview of the folder structure and its contents.
The following files are X-ray imaging data collected on September 28, 2017, using the Med-X system and a Jima phantom at 55 kV peak and 7 Watts.-these files represent radiographs taken on January 13, 2020, using the XRH system, Jima phantom, MoRT target at 55 kVp and varying wattages.

/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleRadioFrom
CTs_5umPixelSize -this directory contains single radiographs taken with a pixel size of 5 micrometers using the Molybdenum rotating (MoRt), and the Reflection target using tungsten (W) and Molybdenum (Mo) metals.
/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices _5umPixelSize -this directory contains single reconstruction slices of the setups mentioned above.Slices are exported from CT volumes and were used for the Edge Modulation function study.
For interpretation of the filenames in the folders listed above please see below and refer to specific files and folders for detailed information and results related to each imaging session: • <xx>kVp or <xx>kV : Imaging at a peak voltage of <xx> kVp.
• _horiz :Reconstruction slices in line with the X-ray beam's propagation direction.
• _vert :Reconstruction slices normal to the X-ray beam's propagation direction and parallel to the detector plane.
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Underlying data
Zenodo."A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications -Underlying Data" https://doi.org/10.5281/zenodo.8083459-This project contains the following underlying data:

Video files and logs
Single-slice and thick-slice roll* source videos are included.Each video is accompanied by a .txtlog that contains information about the source file, slice thickness, and a brief description of the visualization mode.

Videos
Video 1 A video going through the Z stack in single slices.This is a cross-sectional view of the XRH image stack along the XY plane.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.
Video 2 A video going through the Y stack in single slices.This is a cross-sectional view of the XRH image stack along the XZ plane.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.
Video 3 A video going through the X stack in single slices.This is a cross-sectional view of the XRH image stack along the YZ plane.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.

Other data
Some figures presented in this manuscript is a reproduction of previously published material.Readers requiring access to these data are advised to contact the corresponding authors of the original publications.Namely: Please note that this version of the code has had some thirdparty confidential IP supplied to us under NDA by hardware manufacturers removed from it.While efforts have been made to minimize the impact on the main functionality of the code, we strongly recommend users to perform their own thorough bug-check and edits before attempting to run the code.This will help ensure that any potential issues related to the removal of the confidential IP are addressed.
XRHMS is built using Python 3 and uses the django web framework https://www.djangoproject.com/.

Acknowledgments
We acknowledge the support from Nikon X-Tek Systems Ltd, namely Dr Oliver Larkin, Ian Haig and Dr Benny Smit.With thanks to Dr Matthew J. Lawson, Dr Elaine M. L. Ho, and Dr Neil Smyth for providing data and samples.Finally, we acknowledge support from DECTRIS AG (Baden-Daettwil, Switzerland).
Figure 1, Figure 3, Figure 12 and Figure 13 have been created using BioRender (https://biorender.com).Special thanks to Mr Ian Haig; a remarkable man, a visionary and exceptional engineer who passed suddenly and prematurely during the completion of this article.His unwavering support throughout the years was instrumental in laying the foundation of this work.
sometimes also the paraffin emebedding itself (can) induce important tissue shrinkage.Hence, the methodology (combining sample preparation and imaging) cannot be referred to as completely non-destructive.Please rephrase, describe in detail the sample preparation process (and the chemical products used), and mention the (potential) influence of the sample preparation steps on the tissue integrity.
The authors do not refer to the imaging of mineralized tissues (or calcifications).Is this feasible with their methodology?If yes, I would propose to highlight this, as it is an advantage compared to classical 2D histology.Is the imaging of lipids possible?For paraffin embedding, often ethanol or alcohols are used, and they remove the lipids.If this is also the case here, it should be mentioned as a potential limitation.

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A list of advantages and limitations of the methodology would be highly useful.Currently, only the added value is mentioned, but not a lot of attention is given to the current limitations.

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The reviewer highly appreciates the incorporation of the questionnaire and its outcome.
Concerning the importance of various aspects, in the text is seemed as if the people had found the absence of a contrast agent as important as having high contrast in the images.However, from Fig. 2, this aspect was shown as being less important (even almost as important as high-throughput data processing).Perhaps adapt this slightly in the text.

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In the description of µCT imaging, the author mentions that radiographs map density variations, but the attuation of a material is not only dependent on the density, but also the atomic number.Further on, the author mentions that (µ) uses X-ray absorption for contrast.
As attenuation of X-rays concerning not only X-ray absorption but also scattering, I would propose to explain what the materials' inherent attenuation coefficient is, and then refer to attenuation in the text.

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It would be interesting if the authors would also (briefly) comment on the requirements in terms of required spatial resolution (in the design or selection of a µCT device).What is the minimal resolution proposed?Or what would be guidelines for researchers buying a new µCT device?

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It would be interesting to provide information of the time needed for imaging and image analysis.Of course, this is sample dependent, but an indication of both (or a ratio of both) would be very informative.

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As the authors show that paraffin embedding allows improved contrast between different tissue constituents, I miss a clear explanation why the contrast is increased.Which step/part of the sample preparation will induce the contrast differences?what are the mechanisms behind the differential imaging of tissue constituents?What can they visualize (which differences) and what not?How 'specific' are the contrast differences?Is this allowing to differentiate different proteins from each other?Or is it merely the visualization of pore space versus soft tissue?More information on this would be very useful.

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For Case Study 1, how did the authors validate that the difference between the necrotic region and the healthy region?Based on what can they differentiate?Why would the density of a necrotic region be different from that of a healthy region?Do the authors refer to the volume fraction of tissue as density, or to a change in the X-ray attenuation?
In the images (for example Fig. 10), it would be highly informative to indicate what the reader should focus on, or to indicate what is what.Not all readers are experts in the tissues that have been studied.

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For Fig. 11, it would have been useful to also add µCT sections of the samples.

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The reviewer appreciates the sections on data management and storage, which are currently bottlenecks in the entire process.

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In the conclusions, the authors mention the very relevant and highly interesting application of foetal (post-mortem) imaging.The authors mention again that they have pioneered in this, but there are other groups that have been working on this topic already for many years; for example https://www.amsterdamumc.org/en/research/researchers/bernadettede-bakker.htm.Therefore, be careful with the wording of 'pioneering' or 'being unique', or 'unparalleled'.Please avoid these wordings.

Is the rationale for developing the new method (or application) clearly explained? Yes
Is the description of the method technically sound?Yes

Are sufficient details provided to allow replication of the method development and its use by others? Partly
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article?Partly Competing Interests: No competing interests were disclosed.
Reviewer Expertise: 3D X-ray based histology using CECT and cryoCECT imaging; tissue engineering; materials characterization; microCT imaging; mechanical testing; 4D-microCT I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Orestis Katsamenis
Thank you for your thoughtful feedback on our paper.We appreciate your careful consideration and agree that is a few occasions, a more precision in language would be beneficial.Please see below our responses to your comments: In response to your 1 st comment: we have made revisions to better align our statements with the specific context of our work.We acknowledge that 3D X-ray-based histology is a field with contributions from various researchers and labs.We have focused on clearly articulating the uniqueness of our approach within the defined scope of this paper.We have clarified that, in the context of this paper, 3D XRH refers to microfocus computed tomography-based imaging using conventional, attenuation-based μCT for imaging standard unstained FFPE blocks in a lab environment.We would like to emphasise that the term, as used in this paper, encompasses a complete and dedicated workflow for 3D XRH, which is seamlessly integratable with clinical workflows and is not disruptive to them.Nevertheless, we recognise and wholeheartedly support the idea that the term '3D X-ray Histology' should be used to cover a much wider spectrum of approaches and modalities, including phase, contrast, and SR CT.Regarding our comment about it being "the world's first XRH facility", please allows us to respectfully stand by our statement, and provide further clarification.In our understanding, the XRH facility in Southampton is indeed a unique entity globally.It is designed with the explicit purpose of providing X-ray histology imaging services and supporting third-party research initiatives.That is not to say the XRH facility team is the first to conduct micro-CT-based histology research, far from it.While we acknowledge that 3D histology work and research have been conducted by various researchers and labs worldwide, our claim specifically relates to the dedicated infrastructure we have implemented.To clarify, the uniqueness of our facility lies in its structured setup, with the intention of facilitating broader access and utilisation by external users including the NHS.
In response to your 2 nd comment: That is a valid comment and one raised by other reviewers.We want to emphasise that, given the comprehensive nature of the information to be included in our manuscript, we made a deliberate decision not to expand into an exhaustive literature review encompassing all the diverse methods of X-ray CT for histology applications.Instead, we have chosen to present the initial stages of our research journey, which have culminated in the development of what we term as 3D X-ray Histology.We have explicitly addressed this choice at the conclusion of the first paragraph in the introduction of our manuscript, where we encourage readers to explore additional published articles, including those discussing phase-/contrast-enhanced µCT imaging.
In response to your 3 nd comment: Our primary focus when claiming a non-destructive / non-intrusive nature of our X-ray-based histology technique is within the context of classical histology and after the formalin-fixed paraffin-embedded (FFPE) stage.Preceding stages, including sample preparation and paraffin embedding, are elements that fall outside the direct control of 3D XRH, as our approach is designed to handle specimens that have undergone the classical preparation journey.However, we appreciate your observation that classical tissue handling, including the processes leading to FFPE, can affect tissues and introduce disturbances such as tissue shrinkage.These inherent challenges are now acknowledged.Going forward, we have included additional details in the Conclusions paragraph to clarify what's been discussed here.Please refer to the segment beginning with "At this point, it is important to note that, while the non-destructive/non-intrusive nature of XRH imaging holds true for […]" to " […] use relative measures and their study sample groups and controls have undergone the same level of processing" In response to your 4 th comment: We intentionally omitted it from our discussion for two key reasons.Firstly, micro-CT has been a well-established tool for imaging mineralised tissues over several decades, with documented and proven performance across different scales.Our emphasis has been on addressing the challenges associated with imaging soft tissues, arising from them exhibiting minimal X-ray contrast; a long-standing issue in the field.Secondly, in classical histology, mineralised tissues typically undergo demineralisation before further processing.As a result, even histological samples like bone marrow biopsies would have undergone demineralisation by the time they reach the FFPE stage, which is considered our "entry point".There are, of course, FFPE specimens that have mineralisation that is not expected and we successfully image them with slight alterations in the imaging protocol.With regards to lipid imaging, we have recently undertaken work where visualising lipids is exactly what the XRH facility was commissioned for.Although you are correct that FFPE processing strips lipids from tissues, this has been advantageous in the project we have undertaken as "casts" form and thus can be analysed for volumetric and spatial information.It is also compatible with correlative imaging approaches as biomarkers are retained on the cell surfaces of adipocytes and thus can be confirmed as lipid casts postscanning.This application of XRH for this form of imaging is also under preparation for publication.
In response to your 5 th comment: As we image a vast number of samples for a wide range of research projects, it is difficult to give an overarching list of advantages and disadvantages to the generic method, as the methods vary depending on the sample being imaged and other project-specific constrains.For example, shrinkage due to FFPE processing is a disadvantage to some research workflows (e.g.image-based modelling) where absolute length scales are critical to the question.However, in a clinical project where the presence or absence of a diagnostic feature is of interest, the overall shrinkage of said tissue is not considered relevant for evaluation.However, following your recommendation, we have outlined some of the more-prominent limitations associated with the generic facility workflow, which are now added to our manuscript: Imaging Trade-offs: Balancing the trade-off between field of view and resolution, as well as resolution to scanning time (and thus cost), poses a challenge that needs to be carefully considered. 1.

Data Size Challenges:
The size of data generated poses challenges for curation, archiving, and open-data strategies, particularly in studies requiring high n numbers.Additionally, ambiguity regarding what constitutes raw data (raw uncorrected collected radiographs, corrected radiographs, float-point reconstructions, processed 16-bit / 8-bit volumes…?)introduces an extra layer of complexity.

Complexity of Image Analysis:
Managing the intricacies of image analysis, particularly in terms of quantification, presents a significant challenge to many users, especially the ones with no or limited experience to processing of volume data.

3.
Need for Correlative Imaging: Correlative imaging is often required for biomarker 4.
identification, which adds an additional layer of complexity (and cost) to the workflow.Contrast Challenges with Non-Stained Soft Tissues: The majority of non-stained soft tissues, when imaged in a hydrated state exhibit minimal X-ray contrast.For such specimens other approaches are much better suited, and should be considered including phase-contrast imaging, contrast CT and SRCT.

Cost of Relevant Equipment:
The acquisition and maintenance costs associated with the necessary equipment capable of handling XRH data, including software and network infrastructure, can be substantial and lead to underutilisation of the data.For this reason it is important that an XRH facility should be prepared to invest and offer access to processing infrastructure.

6.
Standardisation and DICOM Compatibility: Ensuring standardisation and compatibility with DICOM (Digital Imaging and Communications in Medicine) standards remains a crucial consideration, and one that can unlock clinical compatibility.

7.
In response to your 6 th comment: While we had explicitly mentioned, in accordance with Figure 2, that "High image contrast from unstained tissues [...] was highlighted as being of high importance by **approximately half of the participants**", we have revised our statement to further mitigate the possibility of misinterpretation.We would like once again to emphasise that our findings should be understood within the context of the classical histology workflow.Through this lens, staining prior to sectioning may be perceived as an intrusive or destructive process, and, in some instances, can pose challenges for administrative or practical reasons, particularly when dealing with archival tissue or clinical specimens.
In response to your 7 th comment: The reviewer's observation is indeed accurate.In the relevant section of the text, our intention was to provide a simplified, layperson's description of the basic principles of the technique, aiming to make the content accessible without delving into highly technical details.However, we recognise the importance of ensuring accuracy in the representation of the underlying physics.We have now modify our text accordingly to better convey the complexity of the physics involved, clarifying that the attenuation of X-rays is influenced by other factors beyond material density as well, and we have included a reference to a paper by Withers et al for readers interested in a more indepth understanding.
In response to your 8 th comment: In response to comments raised by another reviewer we have incorporated comprehensive resolution characterisation data into the supplementary materials (see https://doi.org/10.5281/zenodo.10185486).Based on an extensive dataset exceeding 2,500 scans recorded in our system, we can provide details on the mean voxel size, which is 0.021 mm ± 0.023 mm.The median value is 0.012 mm, with minimum and maximum values of 0.001 mm and 0.180 mm, respectively.Our experience indicates that, for micro-anatomy work, a resolution within the range of 5 to 15 µm proves sufficient for the majority of applications.However, it is worth noting that more specialised applications, such as correlative proteomics or genomics, may necessitate higher resolutions.
In response to your 9 th comment: Unfortunately, providing a specific indication or ratio for both imaging and image analysis is challenging due to the highly sample-dependent nature of these processes, which you rightly pointed out.The balance between imaging and analysis time is influenced by factors such as the qualitative versus quantitative nature of the imaging.In general, the time investment for quantitative imaging tends to lean heavily towards the processing stage rather than the acquisition.In the manuscript, we have included a reference to the "most common" imaging protocol for FFPE samples in the part where we are discussing the risk of radiation damage, indicating acquisition times typically ranging from 2 to 5 hours.However, how users handle the data downstream varies significantly across applications, making it challenging to be more specific or provide a standardised ratio for imaging and analysis times.
In response to your 10 th comment: In the realm of soft-tissue imaging, particularly within the context of Formalin-Fixed Paraffin-Embedded (FFPE) tissue, the majority of components consist of organic material with comparable attenuation coefficients.Our non-staining protocol captures minute X-ray attenuation variations introduced by the FFPE processing.The impact of FFPE on tissue physical properties is well-documented, and we could extrapolate the physical effects into the X-ray imaging domain.Formaldehyde, a key component, facilitates the cross-linking of native tertiary protein structures through aldehyde groups within the tissue.This process increases electron density in the crosslinked regions, consequently affecting their X-ray attenuation properties.Regions with heightened cross-linking appear "denser" than their non-or less-cross-linked counterparts.The FFPE process also induces tissue dehydration, resulting in both global and local tissue shrinkage and densification.The dehydration steps in FFPE play a pivotal role in increasing the molecular density per unit space.This is primarily attributed to the removal of substantial quantities of hydrogen and oxygen elements from the sample, leading to a densification of the organic elements proportionate to their hydration level.This densification contributes to subtle distinctions between elements with varying degrees of hydration.These subtle attenuation differences resulting from the FFPE process is what we exploit for contrast.Our intention in future is to systematically gather specific data elucidating the precise contributions of each processing stage to the enhanced contrast in the sample.Furthermore, we aim to investigate the impact of different forms of commonly used fixatives in histological protocols on contrast levels.This comprehensive approach will provide a deeper understanding of the intricate relationships between tissue processing, physical properties, and contrast enhancement, thereby refining our soft-tissue imaging techniques.At this stage we cannot in confidence comment on the specificity of these effects.For cases where specific imaging is crucial, we incorporate correlative conventional histology and immunohistochemistry.The above has now been included in our closing remarks in the Conclusions paragraph.Please refer to the segment beginning with "At this point, it is important to note that, while the non-destructive/non-intrusive nature of XRH imaging holds true for […]" to " […] use relative measures and their study sample groups and controls have undergone the same level of processing" In response to your 13 th comment: Thank you for your comment.The source publication is freely accessible to all, and we believe it would be more appropriate for any interested party to access the link for a comprehensive understanding of the entire work.In addressing your feedback, we have now included a reference to the pertinent figure in the Chulong Zhang Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, China This paper reports an important high-throughput 3D X-ray histology facility.It is indeed remarkable and has rendered many contributions.Here exist only minor concerns and comments: Would it be possible to briefly expand on the principles and methods of 3D X-ray Histology (XRH) at the beginning of the paper? 1.
In Case Study 2, how were the 2D histological slices registered with the 3D XRH? Or, was a 2D slice of the XRH used? 2.
In Case Study 3, could you possibly provide some specific data or tables to support your conclusion, or add more explanation?The current text and visual results alone can be somewhat confusing.

3.
Is the rationale for developing the new method (or application) clearly explained?Yes Is the description of the method technically sound?Yes

Are sufficient details provided to allow replication of the method development and its use by others? Yes
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article?Yes Competing Interests: No competing interests were disclosed.
Reviewer Expertise: X-ray, CT, Medical Physcis, Pathology Image Processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
principles and methods of 3D XRH at the beginning of the.This section now includes an outline and references for further exploration.We hope that this addition enhances the clarity and understanding of XRH for our readers.

Comment 2:
The image show in Figure 10 shows a superposition of the light microscopy image onto a single XRH slice illustration purposes.The registration method used requires the H&E histology slice to be elastically registered onto the μCT volume after the latter has been rigidly re-oriented in space so that XY slices run perfectly parallel to the cutting plane "The first complete system for 3D XRH …" this should refer specifically to the UHS facility.
Med-x prototype: discussion about the choice of metal and accelerating voltage would be valuable here; as well as details about the spatial resolution characterisation (either results or citation).
Customised XRH "significant advantages over static reflection target" what are the advantages?How significant are they?"in ways not previously possible" adding details would help clarifying.
"provide previously inaccessible options for scanning biological samples" also here adding details would help readers in identifying the improvements.
DECTRIS detector and spectral capabilities are mentioned but not really discussed, details in this section would help identifying the advantages and constraints of such development.
Data visualisation: is there a preferred modality/tools from the users? Figure 7 and 9 using panel letters would help referencing to the correct image from caption.
FFPE is a common acronym but should be defined.
Client access: a small table showing field of views and spatial resolution achievable would help in understanding the necessary trade-offs.
In conclusion, I find this is a comprehensive report on a 3D X-ray histology research facility with impressive developments towards making this type of imaging readily accessible to users and clients.The topics covered range from the design of the facility to technical details on the hardware, software tools and case studies.Some points have been identified for the Authors' consideration where the description and discussion can be made more precise and complete to the benefit of the readers.

Is the rationale for developing the new method (or application) clearly explained? Yes
Is the description of the method technically sound?Yes

Are sufficient details provided to allow replication of the method development and its use by others? Yes
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article?milestone as the first complete attenuation-based (i.e.conventional μCT) system explicitly engineered with histology applications in mind, and it was purposefully designed for installation in clinical and hospital settings.This distinction is a critical aspect of our work, as it underscores the unique and pioneering nature of the Med-X prototype.In addition to being the first of its kind, its influence is evident in the fact that the current commercially available system by 3DHISTECH (PANORAMIC-X) shares a substantial portion of its components with the original Med-X prototype, while the enclosure design has been maintained fully.This not only reaffirms the innovative approach we took in developing the system but also underscores the lasting impact it has had on subsequent developments in the field of 3D X-ray Histology.
Comment RE Med-X prototype: Target metals -The metals listed in our manuscript are the ones designated by Nikon as their 'standard' multilateral reflection targets.We have not altered the assortment of reflection target metals in any way.Each metal has its pros and cons, with each one producing a unique X-ray spectrum possible suited to different applications, but this is something we are still exploring; Resolution -In these systems spatial resolution depends upon a number of factors including, geometrical parameters (focus-to-detector / focus-to-object distances), image quality (e.g.presence of scattering, noise or contrast resolution), and of course X-ray source characteristics.For this reason, in our manuscript we are referring to the "smallest" focal-spot size of the electron beam onto the target, as measure of the highest achievable spatial resolution.Characterisation of that property was achieved using resolutions phantoms (JIMA, QRM) in radiography or tomography modes, although other volumetric methods are also applicable (e.g.DOI 10.1088/1361-6501/aa85b7).We have now updated the supplementary data file to include spot-size characterisation data (see https://doi.org/10.5281/zenodo.10185486); The energy range selection is addressed in "Innovative Hardware Advancements" Comment RE DECTRIS integration: This was also raised by Reviewer 1. Please kindly refer to our reply to them.

Comment RE Data visualisation:
In our experience, data visualisation tools are often chosen based on individual user preferences and familiarity with specific packages.Our centre provides high-end computational facilities with a diverse array of both open-source and commercial visualisation and processing suites, including ORS Dragonfly, Avizo, VGStudioMax, Simpleware ScanIP, and Fiji/ImageJ.Users typically make their selections considering factors such as the time available for analysis, the necessity for quantification, and whether XRH visualisation is the end goal or a stepping stone for more advanced analysis, such as image-based modelling.

Comment RE table showing field of views and resolutions:
We understand your point completely.Initially, we considered creating such a table as well.However, upon careful consideration, we realised that a static table would only provide a partial view of the whole picture due to the numerous degrees of freedom involved.For instance, presenting a "best case" scenario for each parameter, such as the smallest focal spot or maximum field of view, might not accurately represent the system's capabilities.Resolution depends on several factors, including the field of view, detector binning, X-ray power / energy (both of which affects focal spot geometry).Additionally, there are options like vertical stitching, offset tomography, and local tomography that can be used to fine-tune the balance between field of view, resolution, and acquisition time.For these reasons, we've decided against presenting such a table as it could potentially lead inexperienced users to draw incorrect conclusions and misrepresent the hardware's capabilities.Instead, we recommend that users engage in a discussion with the imaging scientist who will be managing their session to tailor the parameters to their specific needs effectively.
Competing Interests: No competing interests were disclosed.

Alessandro Olivo
Institute of Healthcare Engineering, University College London, London, England, UK This report provides a thorough description of a unique and important facility that has already benefitted a large number of biomedical researchers and has the potential to benefit more in the future.For these reasons, I have no hesitation in recommending publication of this manuscript in Wellcome Open Research, with only a few optional suggestions.
I'll start with an issue of personal taste -to me it sounds a bit weird for authors to say themselves that "The 3D X-ray Histology (XRH) facility at UHS is a pioneering platform…".I think they could iterate the large number of successful projects they conducted, and they're surely welcome to say that to their knowledge this is the first such facility etc.On a similar note, I wonder whether the sentence "XRH is a microfocus computed tomography-based technology developed upon the proof-of-principle study published in 20151" should be somewhat mitigated, since several studies proposed to use x-rays for virtual histology, especially at synchrotrons.
I would welcome some degree of expansion on the subject of hardware improvements of the scanners -are these limited to the availability of multi-target materials and of the optional rotating Mo target?Could these be encountered more widely in available commercial scannersor, in other words, what is it that makes the MedX and modified 225 "specific" to the imaging of histopathology cassettes?
On a similar note, could the authors explain better when the MedX and when the 225 is usedwhat is the choice based on?
I was impressed with the auto-loader development that makes operations smoother and more cost effective -out of curiosity does filament lifetime then become a bottleneck?
The DECTRIS photon counter is mentioned but very little is said about how it could improve performance -do the authors have any hint on practical improvements?Does it impose a reduction in specimen size, and if so by how much?Are gaps between detector modules an issue?Generic artifact processing is mentioned but as well known ring artifacts are often a prominent one in this context.How are these handled?Is jitter used?
I found the sentence "The standard range of equipment allows imaging at spatial resolution down to 5 μm, although higher resolutions can be achieved with minor modifications of the hardware."(page 16) intriguing -how would this be achieved (considering the resolution ~ 2 x focal spot rule of thumb), and is it currently available?
Another interesting part is the discussion as to whether x-rays can affect subsequent sample analysis.If I understood correctly, the authors cite a synchrotron paper that says they do [54], and a lab-based one that says they don't [7], and seems to attribute this to the different flux rangeshowever I would argue that a good quality CT image depends on the total number of collected photons, which (assuming comparable detector technology is used, which seems to be the case) is probably similar in the two cases, and obtained through much longer scans in the lab case.This possibly goes beyond the scope of this paper, but it would be good to see some additional comment from the authors on this important point.
To possibly end as I started on a matter of personal taste -I would avoid expressions such as "immense potential" (p.18).
While I hope the authors will agree with me that integration of at least some of the above comments would make for a more complete report, I remain convinced that it should be published regardless -I have read it with great interest.
Is the rationale for developing the new method (or application) clearly explained?Yes Is the description of the method technically sound?Yes

Are sufficient details provided to allow replication of the method development and its use by others? Yes
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article?Yes Competing Interests: No competing interests were disclosed.
Reviewer Expertise: x-ray detectors and imaging I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
upon the proof-of-principle study published in 2015" was also referring to a technology using conventional, attenuation-based μCT imaging for imaging standard unstained FFPE blocks in a lab-environment.Up to that point, μCT imaging of such specimens under these conditions was considered extremely challenging if not impossible.However, we do agree that XRH is, and should be, a broader term covering other X-ray modalities including SRμCT, phase-contrast imaging, photon-counting, etc.That for we will be revising this phrase as follows: "XRH in the context of this paper represents a pioneering attenuation-based microfocus computed tomography-base (μCT) technology developed as a result of the proof-ofprinciple study published in 2015.The term extends its applicability to encompass complementary X-ray μCT modalities, such as synchrotron-based μCT, phase-contrast imaging, photon-counting imaging, and more, reflecting its broader scope." ○ On the subject of hardware improvements: we have collaborated with our industrial partner over the years to optimise our systems, aiming to achieve the highest image quality possible when imaging standard soft-tissue FFPE tissue cassette specimens.Our focus has also been on ensuring that these improvements are applicable in a clinical setting.Below, we highlight some of the modifications we have made: Studied and made an adjustment in the electron angle of attack onto the target with the goal of minimising the heel-effect, which can lead to an uneven mean energy profile of the X-ray beam between the bottom and the top of the illumination field.This is important for quantitative imaging and for the consistent visualisation of the tissue across the whole field of illumination.

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Studied and modified the housing in the X-ray source to significantly reduce the generation of secondary sources resulting from the interaction of back-scattered electrons with its housing.While this phenomenon is negligible and causes no problems when materials with high attenuation contrast are imaged, it was causing issues when we were imaging unstained FFPE specimens.These specimens required us to work within a very tight contrast windows, in which "phantom" images of the specimens resulting from the secondary sources could be seen.

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Modified the enclosure of the Med-X prototype to reduce its footprint and its weight.We did that by reducing the maximum energy of the system to 130kVp by using a soft-limited 160kVp source.We also "dressed" its inner components to allow for easy cleaning and reduce the risk of biohazardous contaminations in a clinical environment.

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We modified the standard W rotating target to a Mo rotating target, to allow for higher flux soft X-ray imaging and improving throughput more clinically relevant time-frames.This was paired with the development and deployment in our XRH XT H 225 ST system of our automatic sample exchanger.

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The sample racks for the autoloader system have been specifically designed to allow either the maximum number of standard 30 mm histology cassettes to be loaded or to provide 10 slots which may contain either a standard 30 mm cassette or a 60 mm giga-cassettes.

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We also modified the standard W transmission target to Mo to allow imaging with softer X-rays at much higher resolution.

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A range of these improvements are currently used in the commercially available system (PANORAMIC-X) that was made available to clinical and academic parties for purchasing by 3DHISTECH Ltd in summer 2023.
Regarding the use of each system, XRH XT H 225 system is mainly used for high throughput studies requiring multiple specimens to be scanned using the same protocol.The automatic sample exchanger allows us to set up unsupervised multiday sessions.It is also used for studies requiring higher spatial resolution for a larger field of view.That's possible due to it's 2850 x 2850 139 μm pixel pitch detector compared to the 2000 x 2000 200 μm pixel pitch used in the Med-X.Both the Molybdenum rotating target and Molybdenum transmission target are only available in the XRH scanner.Other than these, the two system are interchangeable in many aspects and often the system selection is based upon availability, user familiarity and other logistics issues.
Moving on to the next comment, the reviewer is correct to assume that the filament lifetime can become the bottleneck.An average lifetime of a filament at XRH-specific imaging setting ranges from 200 -400 h.Our autoloader supports up to 14-specimens so for protocols that require imaging for longer than 14 hr / sample filament lifetime can be an issue.In our experience, if the user is keeping track of beam-on time, which we do, and changes the filament before long sessions, the likelihood of it failing within a single session is minimal.Our protocols are also rarely exceeding 6 h / samples with more of them being around 2 -3 h / sample, which means multiple auto loader racks can be scanned using the same filament.
Regarding the DECTRIS detector, at this stage, we can only speculate about its benefits and our main effort was focused on developing hardware and software to integrate the detector into our systems, and the raw image processing side of things.As mentioned, the field of view on Y-direction (height) is significantly smaller, so realistically for histologicalrelevant resolution helical scanning will need to be used, which is doable in our system.The gaps do pose issues, but image post-processing along with acquisition strategies can mitigate these issues.We are in the process of finalising a paper in which we describe all these developments in detail.Our main hope is that this technology will allow us to use a much narrower beam energy profile and k-edge imaging hopefully further improving the contrast and perhaps the specificity of XRH imaging.Dual-energy imaging will also become possible with single acquisitions.
On the artefact-corrections: Ring artefacts are handled on the acquisition side by using jittering/shuttling of the specimen across a small range of pixels along the X-direction, which aligns with the detector's horizontal dimension.The most common artefacts in this context are cassette artefacts arising from beam-hardening, minor scattering, and in cases of dense objects in the tissue, such as calcium, metal or contrasted sutures, beamstarvation.
The higher resolution mentioned can be achieved using a customised Mo-transmission target.This target supports a spot-size with a diameter in the order of 1.3 μm.With a rule of thumb mentioned achievable spatial resolution is in the order of 2.6 -3 μm.With our 2850 x 2850 dexel detector a specimen with an effective diameter up to 3.5 mm (e.g. a cryo-biopsy or a needle biopsy) can be imaged without resorting to local-tomography.

Regarding the radiation damage:
The reviewer raises a valid point, and it's a matter our community should examine more closely.The comment they made is of course correct.What truly counts in terms of deposited dose, assuming all other factors such as photon energy and sample attenuation characteristics remain comparable, is the actual number of photons interacting with the sample during imaging.The key point we intended to emphasise is the dose-dependence of damage.In many cases, researchers turn to high brilliance synchrotron sources (1e12 photons /s ) to surpass the limitations of laboratory equipment and enhance image quality, rather than matching.This often leads to the use of significantly higher dose protocols.A direct comparison between the two papers referenced in our manuscript in absolute dosimetry terms is challenging for a number of reasons including, beam spectrum profiles (polychromatic vs monochromatic) and tissue condition (fixed/cross-linked vs fresh/hydrated).Nevertheless, based on the limited data available to date, we hold the belief that for FFPE specimens undergoing lab-based XRH imaging, radiation damage is not a significant concern.From internal source characterisation experiments (data not shown) we have measured that for XRH relevant beam parameters (80kVp, 8W, 50 mm source to object distance) we get a Dose rate of c. 30 mGy/s; that is a deposited dose of c. 325 Gy for a 3h scan.Of course, the actual number would differ with filtration, distance from the source, embedding medium, scan duration etc, but they will remain in the same order of magnitude.E.F.Garmana & M. Weik suggest 58 kGy is a low dose (diffraction weighted) while structural studies done using enzymes show structural damage occurring between 0.13 -3.88 MGy (Taberman et al., 2019 in the same); that is 3 to 4 orders of magnitude higher.Given the steadily increasing X-ray flux densities available at Synchrotron facilities, coupled with the practical constraints that often limit scan duration in lab-based XRH (such as cost, availability, and throughput), it's reasonable to assert that tissue studies utilising SRμCT typically entail significantly higher radiation doses.It's worth noting that this doesn't imply that laboratory-based μCT cannot reach these levels of exposure or that SRμCT cannot be optimised for low-dose imaging.What's particularly noteworthy, especially for conventional histology applications, is that Teplov et al. offer initial evidence suggesting that lab-based XRH does not impact protein expression and, consequently, the affinity of tissues for histological stains.Taking into consideration these comments we will be revising the relevant part as follows: "Another point of consideration that should be addressed when discussing experimental planning with the client is the prospect of radiation-induced specimen damage.Such damage has the potential to influence subsequent biochemical analyses, including immunohistochemistry, proteomics, and transcriptomics.Notably, existing research in this area has predominantly focused on synchrotron radiation-based studies, where substantial doses have been found to impact tertiary protein structures [54].Nevertheless, for laboratory-based radiation doses, especially within the context of the low-dose protocols typically used for soft tissue imaging in our facility (0.1 -5.0 kGy), there appears to be minimal impact on the tissue, especially concerning the affinity of tissues for histological stains [7]."

Figure 2 .
Figure 2. Summary of users' survey.The survey collected information from both domestic (UK) and global collaborators and prospective users of the XRH facility, with the objective of comprehending the distinct requirements of the user community.The XRH facility was designed and tailored to cater to these demands; (a): Research areas of Participants, (b): Importance of various aspects of 3D XRH to participants' work, (c) Importance of various opportunities emerging with 3D XRH to participants' work.

Figure 4 .
Figure 4. 3D XRH facility at the University of Southampton.The facility is located at the University Hospital Southampton, and it is jointly run by the μ-VIS X-ray Imaging Centre and the Biomedical Imaging Unit.(Top) XRH XT H 225 scanning room.(Bottom) The Med-X prototype scanning room.Both μCT systems were custom-specified, designed, and built in a collaboration between the University of Southampton and Nikon X-Tek Systems Ltd (Tring, UK).

Figure 5 .
Figure 5.The sample exchange autoloader system utilized for uninterrupted micro-computed tomography (μCT) scanning at the University of Southampton's 3D X-ray Histology (XRH) facility.The top panel shows a 14-slot rack capable of holding samples with a 30 mm diameter per slot, while the bottom panel displays a 10-slot rack with dual slots accommodating samples with 30 mm and 60 mm diameters.Seven conventional histology cassettes are loaded in the bottom rack for scanning.

Figure 6 .
Figure 6.Multiplanar 2D viewing of 3D XRH data.(Left) The xy-plane is defined by convention as the plane parallel to the histology cassette, where one xy-slice (blue plane on the localiser shown on the right) is shown here.(Middle) One yz-slice at the top (red on the localiser shown on the right) and one xz-slice at the bottom (green on the localiser sown on the right) that have been selected.They are orthogonal to each other and to the xy-plane.(Right) Localiser that combines one xy-slice and the position of the other ortho-planes (yz-slice: red; xz-plane: green).Using ortho-plane viewing, the user can scroll through the depth, width, and height of the specimen, zoom, pan, and perform dimensional measurements.

Figure 7 .
Figure 7. Thick-slice 2.5D viewing of 3D XRH data.Top row shows a whole-block XRH image of a head and neck solid tumour specimen, while the bottom row displays a 10 x 10 mm 2 detail view of the lower-right area of the tissue.(Left) Single slice view, (Middle) 20-slice average intensity projection "AVG", (Right) 20-slice maximum intensity projection "MIP".

Figure 8 .
Figure 8. Whole-block XRH imaging of a conventionally prepared FFPE sample.Histology cassette not rendered for clarity.(Left) Volumetric rendering showing the tissue sample inside the wax (embedding medium of the sample).(Middle) volumetric rendering of the tissue.(Right) volumetric rendering of local 3D tissue thickness, where the colour bar ranges from 0.01 mm (blue) up to 0.31 mm (red).Images generated using Dragonfly (Object Research Systems).

Figure 9 .
Figure 9. Quantitative imaging for cancer research.(Top section) Average intensity projection (AvgIP) and 3D volume rendering of the tumours studied in 25; (Bottom section): 3D (volume) rendering of the segmented 'necrotic core' of two tumours treated with two different formulation, showing the extent and the morphology of the necrotic core in space, rendered along with a representative central 2D μCT slice.Quantitative analysis allowed to accurately access the volume of the necrotic core (29.83mm 3 vs.13.16mm 3 ) as well as the core to total tumour ratio: 2.58% of the tumour volume in the DOX+CUR solution-treated tumour was necrotic compared to 5.29% on the DOX+CUR peptide hydrogel-treated one.

Figure 10 .
Figure 10.Correlative XRH and conventional histology imaging of a human head and neck tumour specimen.Specimen was scanned on a cassette at 15 μm voxel (3D pixel) size without staining.Serial sections were taken after μCT and stained with H&E.Top row: Side-by-side visualisation of matching XRH and conventional histology images on the left (a,b); Augmented tomography in the middle (c) is made possible by fusing conventional histology images with XRH data to enhance the specific information presented by the mainly structural μCT data; Volumetric visualisation of the whole tissue block on the right (d) offers 3D context and overview of the microanatomy.Bottom row: Close-up images on the central part of the volume shown in (a) & (b).Single XRH slice on the left (e), H&E slice in the middle (f), and an MIP image (g) uncovering the volumetric development of micro-vasculature on the right.

Figure 11 .
Figure 11.3D XRH-based mathematical modelling.3D model simulation result for flow in an intralobular and subpleural lung geometry.Graphical representations of human lung tissue volumes of interest at an intralobular location and a subpleural location (a and b, respectively).Blood vessels = blue, lymphatic vessel = green.Resultant mathematical model solution for the static pressure (Pa) within vasculature at an intralobular location and a subpleural location (c and d, respectively).Streamlines of Darcy's velocity field into the interstitium are shown in red.The interstitial pressure results have been removed from view so the streamlines could be visualised.The volume of interests has a dimension of 830 × 830 × 830 μm 3 .A visual outline of the segmentation process of the relevant features used for generating the model shown here are illustrated in figure 2 in 30, where the segmented features (lymphatic-and blood-vessels) are rendered against a histological slice and 2D and 3D μCT images.

Figure 12 .
Figure 12.The X-ray Histology Management System (XRHMS).The XRHMS is designed to be the control centre of all operations at the 3D XRH facility.Typical inputs include new project entries, client/user enquiries as well as sample logging and tracking.Importantly, XRHMS automatically handles incoming imaging data, generates metadata, organises, and assigns these data to samples and projects.

Figure 13 .
Figure 13.XRH facility access request workflow.Stage 1: An initial enquiry is submitted to the XRH team, which is getting in contact with the client to discuss project details and assess feasibility.Stage 2: If feasibility criteria are met, samples are sent to the facility and project commences.Stage 3: Imaging, reporting, and data release to the client.Depending on the project design, this stage may also include complementary sample processing, conventional histology, correlative imaging and/or bespoke data analysis.

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20170928_MEDX_1642_JIMA_55kVp7W-2.tif • 20170928_MEDX_1642_JIMA_55kVp7W.tif• 20170928_MEDX_1642_JIMA_55kVp7W.tif.profile.xmlA QRM MicroCT bar pattern phantom PDF document and its specifications: • QRM-MicroCT-Barpattern-Phantom.pdfGraphs PNG file showing the calculated focal-spot size as a function of the X-ray power (W) for the Molybdenum rotating target calculated using Edge Modulation function testing.The performance is then compared with the performance of the Reflection target across the same range of powers.Raw data can be found in XRH_QRM_Refl-vs-Rot-Target-Comparison_SingleReconSlices_5umPixelSize folder.Test performed in July 2021: / XRH-XT-H-225-ST_FocalSpots -this directory contains radiographic data collected using the XRH system with a JIMA phantom and MoRt (Molybdenum rotating), TT (Transmission), and Reflection targets: • 20200113_XRH_Jima test MoRT 55kV 15W.tif, 20200113_XRH_Jima test MoRT 55kV 30W.tif, etc.

Video 5 Video 6 Video 7
Average Intensity Projection (AIP) of the sample through the Histologically relevant plane.This is a 2D visualisation rendering the Average Intensity of 20x single XY slices along the z-axis of the stack.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.Maximum Intensity Projection (MIP) of the sample through the Histologically relevant plane.This is a 2D visualisation rendering the Maximum Intensity of 20x single XY slices along the z-axis of the stack.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.Standard deviation projection of the sample going through the histologically relevant plane.This is a 2D visualisation rendering the Standard Deviation of 20x single XY slices along the z-axis of the stack.XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Reviewer Report 12
October 2023 https://doi.org/10.21956/wellcomeopenres.21783.r66178© 2023 Olivo A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Fig 7
Fig 7 add letters (a-f) to panels, add box in a-c to indicate area where d-f where extracted from.Same for fig 10 (box only).
/Z and WT109682MA) and the University of Southampton.The X-ray Histology facility is located at the University Hospital Southampton and sits within the University of Southampton μ-VIS X-ray Imaging Centre https://muvis.org and the Biomedical Imaging Unit https://www.southampton.ac.uk/ biu/.
License: Apache License 2.0 Software disclaimer: This is the underlying code for the xrhms written as part of the X-ray Histology Project https://www.xrayhistology.org/funded by the Wellcome Trust (grant 212940/ Z/18