- •
Long axial FOV data bring new imaging opportunities and potential of improved reconstruction quality, but also challenges caused by dramatic increase of data sizes and challenges given by the change and variations of data characteristics with increased acceptance angles.
- •
Good TOF resolution and considerably increased sensitivity of total-body imaging scanners allow novel and improved modeling and ways of processing their data.
- •
Total-body PET enables simultaneous dynamic imaging of the entire
3D/4D Reconstruction and Quantitative Total Body Imaging
Section snippets
Key points
Basics
Total-body PET scanners have a huge number of lines of response (LORs). For example, the uEXPLORER scanner (United Imaging Healthcare, Shanghai, China) has more than half a million individual crystals, forming more than 90 billion LORs.1,2 With time-of-flight (TOF) information, the number of elements in a TOF sinogram is more than 1 trillion, which is far greater than the number of coincidence events that could be detected in a regular scan. For example, a 1-hour dynamic scan following a 256
Efficient reconstruction using direct image reconstruction for time-of-flight data framework
Although list-mode reconstruction has the advantage of facilitating straightforward and accurate modeling (in forward projection) for each acquired event, this is at the cost of having to separately calculate forward- and back-projection operations for each individual event, leading to high computational demands. Direct image reconstruction for TOF data (DIRECT)22 represents an alternative, efficient, reconstruction approach taking advantage of the considerably decreased angular sampling
Frame-by-Frame Reconstruction Using the Kernel Method
In the setting of dynamic PET imaging, the expectation of the LOR measurement in the time frame is described byfor with the total number of time frames.a Similar to the kernel method for standard dynamic
Summary and future prospects
Total-body PET provides challenges and opportunities for 3D/4D PET image reconstruction. On the one hand, the large dataset size presents a daunting challenge in computation. Incorporation of the long oblique LORs in reconstruction also requires an accurate model of the response function and proper handling of the correction factors, such as normalization and scatter correction. On the other hand, the high photon-detection sensitivity of total-body PET provides sufficient count density to take
Acknowledgments
Dr J. Qi reports support by grants from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the National Cancer Institute (NCI) of the National Institutes of Health (NIH) under Award Nos. R01EB000194, R21EB026668, R01CA206187 and funding through a sponsored research agreement by Canon Medical Research USA. Dr G. Wang reports support by grants from NCI and NIBIB under Award Nos. K12 CA138464, R01CA206187, P30CA093373, and R21 EB027346. Dr S. Matej reports support by
References (57)
- et al.
X-ray-based attenuation correction for positron emission tomography/computed tomography scanners
Semin Nucl Med
(2003) - et al.
PET iterative reconstruction incorporating an efficient positron range correction method
Phys Med
(2016) - et al.
DeepPET: a deep encoder-decoder network for directly solving the PET image reconstruction inverse problem
Med Image Anal
(2019) - et al.
Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI
Neurocomputing
(2017) - et al.
First human imaging studies with the EXPLORER total-body PET scanner
J Nucl Med
(2019) - et al.
Total-body dynamic reconstruction and parametric imaging on the uEXPLORER
J Nucl Med
(2020) - et al.
List-mode maximum-likelihood reconstruction applied to positron emission mammography (PEM) with irregular sampling
IEEE Trans Med Imaging
(2000) - et al.
List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET
IEEE Trans Med Imaging
(1998) - et al.
List-mode likelihood
J Opt Soc Am A Opt Image Sci Vis
(1997) - et al.
Iterative reconstruction techniques in emission computed tomography
Phys Med Biol
(2006)
Iterative image reconstruction for positron emission tomography based on a detector response function estimated from point source measurements
Phys Med Biol
Sinogram blurring matrix estimation from point sources measurements with rank-one approximation for fully 3-D PET
IEEE Trans Med Imaging
Fast and efficient fully 3D PET image reconstruction using sparse system matrix factorization with GPU acceleration
Phys Med Biol
Efficient fully 3D list-mode TOF PET image reconstruction using a factorized system matrix with an image domain resolution model
Phys Med Biol
Method for transforming CT images for attenuation correction in PET/CT imaging
Med Phys
Modelling random coincidences in positron emission tomography by using singles and prompts: a comparison study
PLoS One
Improving the singles rate method for modeling accidental coincidences in high-resolution PET
Phys Med Biol
Correction methods for random coincidences in fully 3D whole-body PET: impact on data and image quality
J Nucl Med
Parallax error in long-axial field-of-view PET scanners-a simulation study
Phys Med Biol
Quantitative image reconstruction for total-body PET imaging using the 2-meter long EXPLORER scanner
Phys Med Biol
Developments in component-based normalization for 3D PET
Phys Med Biol
Model-based normalization for iterative 3D PET image reconstruction
Phys Med Biol
Optimal whole-body PET scanner configurations for different volumes of LSO scintillator: a simulation study
Phys Med Biol
PennPET explorer: design and preliminary performance of a whole-body imager
J Nucl Med
Efficient 3-D TOF PET reconstruction using view-grouped histo-images: DIRECT - Direct Image Reconstruction for TOF
IEEE Trans Med Imaging
Image-reconstruction of data from super PETT I: a first-generation time-of-flight positron-emission tomograph (reconstruction from reduced-angle data)
IEEE Trans Nucl Sci
Fast reconstruction of 3D time-of-flight PET data by axial rebinning and transverse mashing
Phys Med Biol
GPU-accelerated forward and back-projection with spatially varying kernels in 3D DIRECT TOF PET reconstruction
IEEE Trans Nucl Sci
Cited by (8)
Total-body PET
2022, Nuclear Medicine and Molecular Imaging: Volume 1-4Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists’ Perspective
2022, PET ClinicsCitation Excerpt :Similar algorithms can be incorporated into the NM imaging workflow to further improve the accuracy of scan range delimitation in PET/CT. During the image acquisition and scanning stage, it is also vital to investigate how digital equipment might be used to increase imaging quality and efficiency.31,32 AI's applications for accelerating scanning time and for dosage reduction31 are also promising avenues that can be applied to NM and other imaging modalities.
EXPLORing Arthritis with Total-body Positron Emission Tomography
2023, Seminars in Musculoskeletal RadiologyCharacterization and Assessment of Projection Probability Density Function and Enhanced Sampling in Self-Collimation SPECT
2023, IEEE Transactions on Medical Imaging
- 1
Equal contributions.