Ultrafast Brain MRI at 3 T for MS: Evaluation of a 51-Second Deep Learning-Enhanced T2-EPI-FLAIR Sequence

In neuroimaging, there is no equivalent alternative to magnetic resonance imaging (MRI). However, image acquisitions are generally time-consuming, which may limit utilization in some cases, e.g., in patients who cannot remain motionless for long or suffer from claustrophobia, or in the event of extensive waiting times. For multiple sclerosis (MS) patients, MRI plays a major role in drug therapy decision-making. The purpose of this study was to evaluate whether an ultrafast, T2-weighted (T2w), deep learning-enhanced (DL), echo-planar-imaging-based (EPI) fluid-attenuated inversion recovery (FLAIR) sequence (FLAIRUF) that has targeted neurological emergencies so far might even be an option to detect MS lesions of the brain compared to conventional FLAIR sequences. Therefore, 17 MS patients were enrolled prospectively in this exploratory study. Standard MRI protocols and ultrafast acquisitions were conducted at 3 tesla (T), including three-dimensional (3D)-FLAIR, turbo/fast spin-echo (TSE)-FLAIR, and FLAIRUF. Inflammatory lesions were grouped by size and location. Lesion conspicuity and image quality were rated on an ordinal five-point Likert scale, and lesion detection rates were calculated. Statistical analyses were performed to compare results. Altogether, 568 different lesions were found. Data indicated no significant differences in lesion detection (sensitivity and positive predictive value [PPV]) between FLAIRUF and axially reconstructed 3D-FLAIR (lesion size ≥3 mm × ≥2 mm) and no differences in sensitivity between FLAIRUF and TSE-FLAIR (lesion size ≥3 mm total). Lesion conspicuity in FLAIRUF was similar in all brain regions except for superior conspicuity in the occipital lobe and inferior conspicuity in the central brain regions. Further findings include location-dependent limitations of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as artifacts such as spatial distortions in FLAIRUF. In conclusion, FLAIRUF could potentially be an expedient alternative to conventional methods for brain imaging in MS patients since the acquisition can be performed in a fraction of time while maintaining good image quality.


Introduction
Echo-planar imaging (EPI) represents a very fast acquisition technique in magnetic resonance imaging (MRI).It was first described by Mansfield in 1977 [1] and has since been refined.In [2], Poustchi-Amin et al. reviewed the main principles of EPI: to reduce the number of excitation pulses, thus reducing the number of repetition time (TR) periods by acquiring multiple k-space lines per shot, i.e., during a repetition period.It can be performed using only one shot (single-shot EPI), which means that k-space is filled completely following one excitation, or otherwise using multiple shots (multi-shot EPI), i.e., a particular fraction (obeying a regular sampling pattern along the phase-encoding direction) of k-space is filled with data within each TR period.This can be achieved by generating repetitive echo train signals.Apart from diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), functional MRI (fMRI), and occasionally T2*-weighted (T2*w) imaging [3], EPI has not been integrated into other routine brain MRI sequences such as fluid-attenuated inversion recovery (FLAIR) for the assessment of inflammatory lesions due to drawbacks in terms of image quality and artifacts.
Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system (CNS).Neuro-inflammation thereby results in demyelination and axonal damage with reactive gliosis and lesion formation leading to clinical disability progression [4].MS is the most frequent demyelinating disease worldwide, with the highest prevalence levels in Europe and North America (over 100/100,000 inhabitants) [5].Relapsing-remitting multiple sclerosis (RRMS) is the most frequent initial course of MS, and women are affected two to three times (or more) as often as men [5].According to the McDonald criteria and its later revisions, dissemination of CNS lesions in time and space needs to be fulfilled for diagnosis, thus inevitably requiring appropriate MRI examinations [6][7][8][9].Again, the diagnosis of MS entails further scans for disease activity and treatment monitoring [10].For this purpose, a FLAIR sequence is an essential tool for cerebral imaging, and the availability of a fast imaging technique would be relevant in that context, given the high prevalence of this disease and the corresponding costs.
In 1999, Filippi et al. analyzed MS lesion detectability in ultrafast EPI-FLAIR images compared to conventional fast spin-echo FLAIR images.They observed similar lesion numbers for lesions that were greater than ten millimeters in long-axis diameter.Detection of smaller lesions proved inferior using EPI-FLAIR sequences [11].Owing to specificity reasons, there has been support for a size threshold of white matter lesions to allow the diagnosis of MS [12,13].According to the McDonald criteria, hyperintense areas are referred to as lesions if they are greater than three millimeters in long axis [6][7][8][9].Grahl et al. recently reviewed those stipulations about a lesion size threshold and confirmed three millimeters to be a reasonable threshold to account for diagnostic criteria in MS for three-dimensional (3D) MRI acquisitions at 3 tesla (T) [14].However, the effectiveness of EPI-FLAIR sequences has been far from meeting those targets and has therefore not been a suitable option for routine MRI scans.
Enabled primarily by DL, the acceleration provided by these techniques has redoubled interest in further investigations.Particularly, the integration of several of these methods with EPI-based imaging has led to the development of ultrafast multi-contrast protocols, providing all contrasts required in an emergency setting (T1, T2, T2*, T2-FLAIR, DWI) [39][40][41][42][43][44][45][46].There is also an approach to incorporate various contrasts in one sequence [47][48][49][50][51][52].So far, these methods have not targeted clinical applications that rely to some extent on high-resolution data.Also, current research that focuses on EPI-FLAIR in particular is limited and primarily addresses stroke patients and pediatric patients [53][54][55][56].In-depth evaluations of MS patient brain images that were acquired using DL-enhanced EPI-FLAIR acquisition techniques have not been conducted to date to our knowledge.Although 3D-FLAIR is clearly given preference over 2D-FLAIR for diagnosis and monitoring of MS [57,58], the purpose of this part of our study was to evaluate whether a 2D ultrafast  29  years Distribution between sexes 71% male (n = 12), 29% female (n = 5) Note.One patient underwent a second follow-up MRI examination six months later during the investigation period and is included twice.Years and percentages are rounded to the whole numbers.

Imaging Protocol and Image Acquisition
All examinations were conducted using a 3 T MRI scanner (see Section 2.1).We used a 20-channel head and neck coil and applied our standard protocol for patients with MS, incorporating the following contrasts: 3D T1 magnetization prepared rapid gradient echo (MPRAGE), contrast-enhanced 3D T1-MPRAGE, contrast-enhanced axial T1 turbo/fast spin echo (TSE), infratentorial axial T2-TSE, axial DWI, 3D double inversion recovery (DIR), and 3D-T2-FLAIR (FLAIR3D).In three patients, an axial standard T2-TSE-FLAIR (FLAIRTSE) sequence was added as well.Additionally, we acquired the ultrafast axial T2w FLAIRUF research sequence in all patients, along with other ultrafast sequences that are not in the scope of this article (i.e., two native axial T1w contrasts, two contrast-enhanced  29  years Distribution between sexes 71% male (n = 12), 29% female (n = 5) Note.One patient underwent a second follow-up MRI examination six months later during the investigation period and is included twice.Years and percentages are rounded to the whole numbers.

Imaging Protocol and Image Acquisition
All examinations were conducted using a 3 T MRI scanner (see Section 2.1).We used a 20-channel head and neck coil and applied our standard protocol for patients with MS, incorporating the following contrasts: 3D T1 magnetization prepared rapid gradient echo (MPRAGE), contrast-enhanced 3D T1-MPRAGE, contrast-enhanced axial T1 turbo/fast spin echo (TSE), infratentorial axial T2-TSE, axial DWI, 3D double inversion recovery (DIR), and 3D-T2-FLAIR (FLAIR 3D ).In three patients, an axial standard T2-TSE-FLAIR (FLAIR TSE ) sequence was added as well.Additionally, we acquired the ultrafast axial T2w FLAIR UF research sequence in all patients, along with other ultrafast sequences that are not in the scope of this article (i.e., two native axial T1w contrasts, two contrast-enhanced axial T1w contrasts, axial DWI, and an axial sequence providing a T2*w and a T2w contrast).All native sequences were acquired prior to the contrast agent application, and the standard protocol sequences were acquired in advance of the ultrafast sequences.The acquisition parameters for all three T2w FLAIR sequences (i.e., FLAIR UF , FLAIR TSE , and FLAIR 3D ) are given in Table 2.By using the FLAIR UF sequence, which takes 0:51 min, the acquisition time can be reduced to almost a sixth of the time required for the FLAIR 3D sequence (4:57 min) and nearly a third of the time required for the FLAIR TSE sequence (2:22 min).Note.FLAIR = fluid-attenuated inversion recovery; FLAIR UF = ultrafast FLAIR sequence; FLAIR TSE = turbo/fast spin-echo FLAIR sequence; FLAIR 3D = three-dimensional FLAIR sequence; TR = repetition time; TE = echo time; TI = inversion time; FOV = field-of-view; EPI = echo-planar-imaging; TSE = turbo/fast spin echo; SPACE = sampling perfection with application-optimized contrasts using different flip angle evolution; VFA = variable flip angles; A = anterior; P = posterior; R = right; L = left; DL = deep learning; GRAPPA = generalized autocalibrating partial parallel acquisition. 1Values refer to the sagittal orientation.Slice thickness of the axially reconstructed images used for side-by-side comparisons (FLAIR 3Da ) = 3 mm.
After acquisition of the data and extraction of relevant clinical data, imaging data were de-identified using RSNA Clinical Trial Processor (CTP) software (RSNA CTP Java Version 1.8, Radiological Society of North America, Oak Brook, IL, USA) for further evaluation within this trial.

FLAIR UF Sequence
The FLAIR UF sequence examined in this study was a T2w inversion-recovery doubleshot spin-echo EPI sequence; i.e., k-space data for each image were acquired in two shots with interleaved phase-encoding patterns.Each shot consisted of a spin-echo inversion recovery excitation module followed by a 64-echo readout train.In all three FLAIR sequences (FLAIR UF , FLAIR TSE , and FLAIR 3D ), the PI acceleration factor was two.
In order to alleviate the deterioration in image quality caused by the above-mentioned acceleration techniques, the FLAIR UF sequence has several novel features.One of them is a DL-enhanced processing technique that employs a machine learning-based reconstruction to decrease image noise and residual aliasing [43][44][45]59].Specifically, the method utilizes an unrolled gradient descent network to produce a high-SNR prior image, which is then used to regularize a final conjugate-gradient SENSE reconstruction (see [43] for further details).The network was trained on data acquired with a 20-channel head matrix coil at 3 T [43,44].However, the clinical patient images included in our study were not part of the training data.The reconstruction method requires coil sensitivity maps based on the eigenvector-based iterative self-consistent parallel imaging reconstruction technique (ESPIRiT) [60].Those maps are calculated using fast low-angle shot (FLASH)-based autocalibration scans acquired prior to the slice scans.Furthermore, geometric coil compression was utilized to improve reconstruction performance, reducing reconstruction times [61].Another novel feature implemented is magnetization transfer (MT) preparation to improve tissue contrast, as described in [62].In addition, the FLAIR UF sequence utilizes the following techniques to improve image quality, amongst others: field map-based ge-ometric distortion correction [63,64], phase correction scans to mitigate residual Nyquist ghosting, flow attenuation gradients to weaken signals from flowing or pulsating fluids, and automated interleaving of inversion and acquisition modules to further optimize the scan efficiency.

Image Evaluation
Inflammatory lesions and image quality were evaluated by one reader (M.S.), and all ratings were verified by an experienced neuroradiologist (B.B.) in a consensus reading with the first reader for a final decision.

Lesion Assessment
All inflammatory lesions recorded were documented and listed.Identifiers were assigned, using all sequence contrasts available, particularly axial reconstructions including 3D multiplanar reconstructions (MPRs) of FLAIR 3D , DIR, and T1-MPRAGE, plus an axial T2-TSE sequence contrast.The utilization of all these contrasts was referred to as the gold standard (GS).Accordingly, the total amount of lesion counts was referred to as truepositives using GS (TP GS ).To each TP GS lesion, the following attributes were assigned (see Figure 2): (1) clearly detectable as a lesion using only the axial reconstruction of FLAIR 3D (FLAIR 3Da ), i.e., true-positive in FLAIR 3Da (TP 3Da ), or not clearly detectable as a lesion using only FLAIR 3Da , i.e., false-negative in FLAIR 3Da (FN 3Da ); (2) clearly detectable as a lesion using only the FLAIR UF images (TP UF ), or not clearly detectable as a lesion using only the FLAIR UF images (FN UF ); (3) lesion size, i.e., lengthwise axial diameter (mm, to the nearest tenth); ( 4   The following attributes were assigned to only a particular subset of the TP GS lesions (see also Figure 2 [6][7][8][9]13,65] (characteristic [periventricular, juxta-/cortical, infratentorial] or not characteristic) were both assigned to large (≥3 mm) TP GS lesions only; (2) lesion detectability in FLAIR TSE (TP TSE or FN TSE ) was assigned to the subset of TP GS recorded with FLAIR TSE (tseTP GS ); (3) lesion conspicuity in FLAIR UF compared with FLAIR 3Da counterpart, using an ordinal five-point Likert scale (1 = better/larger in the FLAIR UF images; 2 = equal; 3 = better in the FLAIR 3Da images, but classified as a lesion using only the FLAIR UF images; 4 = better in the FLAIR 3Da images and classified as no lesion using only the FLAIR UF images; 5 = FLAIR 3Da lesion that is not at all visible in the FLAIR UF images), was only assigned to the TP 3Da lesions; (4) presumed causes for not being detected in FLAIR UF were only assigned to the FN UF lesions; (5) presumed causes for not being detected in FLAIR 3Da were only assigned to the FN 3Da lesions; (6) presumed causes for not being detected in FLAIR TSE were only assigned to the FN TSE lesions.

Image Quality Assessment
For the purpose of general image quality assessment, three parameters were employed: signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifacts.FLAIRUF, FLAIR3Da, and FLAIRTSE slice series were each rated for SNR and CNR on an ordinal fivepoint Likert scale (1 = very good; 2 = good; 3 = acceptable; 4 = mediocre but diagnostic; 5 = poor and non-diagnostic).Moreover, all artifacts in the FLAIRUF, FLAIR3Da, and FLAIRTSE images were assessed based on quantity and quality.To this end, FLAIRUF, FLAIR3Da, and FLAIRTSE sequences were classified as to their limitations of diagnostic information in the respective artifact region, using an ordinal five-point Likert scale (0 = No artifact; 1 = Artifact exists, but diagnostic information is not limited; 2 = Artifact exists, and diagnostic information is slightly limited in the artifact region; 3 = Artifact exists, and diagnostic information is limited in the artifact region; 4 = Artifact exists, and diagnostic information is severely limited in the artifact region).
To assess location-dependent SNR and CNR within the FLAIRUF images, the following attributes were assigned to each TPGS lesion (see previous Section): (1) SNR in the vicinity of the lesion (standard or substandard; with reference to the average SNR in

Image Quality Assessment
For the purpose of general image quality assessment, three parameters were employed: signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifacts.FLAIR UF , FLAIR 3Da , and FLAIR TSE slice series were each rated for SNR and CNR on an ordinal five-point Likert scale (1 = very good; 2 = good; 3 = acceptable; 4 = mediocre but diagnostic; 5 = poor and nondiagnostic).Moreover, all artifacts in the FLAIR UF , FLAIR 3Da , and FLAIR TSE images were assessed based on quantity and quality.To this end, FLAIR UF , FLAIR 3Da , and FLAIR TSE sequences were classified as to their limitations of diagnostic information in the respective artifact region, using an ordinal five-point Likert scale (0 = No artifact; 1 = Artifact exists, but diagnostic information is not limited; 2 = Artifact exists, and diagnostic information is slightly limited in the artifact region; 3 = Artifact exists, and diagnostic information is limited in the artifact region; 4 = Artifact exists, and diagnostic information is severely limited in the artifact region).
To assess location-dependent SNR and CNR within the FLAIR UF images, the following attributes were assigned to each TP GS lesion (see previous Section): (1) SNR in the vicinity of the lesion (standard or substandard; with reference to the average SNR in FLAIR UF ); (2) CNR in the vicinity of the lesion (standard or substandard; with reference to the average CNR in FLAIR UF ).
In order to compare lesion detection in FLAIR UF with lesion detection in FLAIR 3Da , contingency tables were created, correlating TP and FN lesion counts.Also, the counts and presumed causes of FN and FP lesions were contrasted.The sensitivity values (TP/[TP + FN]) and positive predictive values (PPVs; TP/[TP + FP]) as to lesion detection were specified including their 95% Clopper-Pearson confidence intervals (CIs), and FN lesion counts were compared using McNemar's test (Excel).The same analyses were performed to compare the lesion detection in FLAIR UF with the lesion detection in FLAIR TSE .To ascertain factors that affect the lesion conspicuity within the FLAIR UF images, conspicuity ratings were compared, and grouped by lesion size and location, using the Wilcoxon rank-sum test and the Kruskal-Wallis test (SPSS).With the aim to investigate whether conspicuity ratings might possibly have been biased by lesion size variations among the location groups, lengthwise diameters of the TP 3Da lesions were reported for each location (mean/95% CI, standard deviation [SD], median) and compared using the Kruskal-Wallis test (SPSS).Moreover, large TP 3Da lesions (≥3 mm) for each brain region were divided into wide (≥2 mm) and narrow (<2 mm) lesions and were further differentiated by conspicuity ratings.
For the purpose of contrasting the ordinal SNR, CNR, and artifact ratings in the FLAIR UF and FLAIR 3Da image series, results were reported as median plus interquartile range (IQR) as well as mean ± SD; a Wilcoxon signed-rank test was also performed (SPSS).Similarly, results were reported for comparing FLAIR UF with the FLAIR TSE image series.To ascertain positional factors that affect the SNR and CNR within the FLAIR UF images, the dichotomous SNR and CNR ratings were grouped by location; the proportions of the substandard ratings were determined for each group including 95% Clopper-Pearson CIs.Groups were compared using the chi-squared test (Excel).
In [66], Bender et al. stated that multiple testing corrections are not necessarily required for exploratory trials generating (diverse) hypotheses.Accordingly, adjustment for multiple comparisons was waived for this study.A p-value < 0.05 was considered statistically significant.

Image Acquisitions and Lesions
The image data include seventeen FLAIR UF sequences, seventeen corresponding FLAIR 3D sequences, and three additional FLAIR TSE sequences, which could all be acquired from seventeen MRI examinations in sixteen different RRMS patients (twelve males, five females).The patients' mean age was 29 years, with ages ranging from 21 to 60 years (see Table 1).Lesion counts are schematically visualized in Figures 4 and 8 and listed in detail in Figures 5 and 9.In total, we counted 568 true-positive lesions using the gold standard (TP GS ), i.e., actual inflammatory brain lesions detected using all contrasts available, as described in Section 2.4.1.Of the 568 existing lesions, 288 (50.7%) were categorized as 'large lesions' (≥3 mm total), and 280 (49.3%) were classed as 'small lesions' (<3 mm).Of the 288 large lesions, 171 (59.4%) were named as 'wide lesions' (width ≥2 mm), and 117 (40.6%) were labeled 'narrow lesions' (width < 2 mm).Taking account of all 568 lesions, there were 542 (95.4%) of them that could be detected using only the FLAIR 3Da images, hence referred to as TP 3Da lesions.Of the 542 TP 3Da lesions, 274 (50.6%) were large and 268 (49.4%) were small.The count of wide large TP 3Da lesions was 163 (59.5%), and the count of narrow large TP 3Da lesions was 111 (40.5%).The mean count of TP GS lesions and standard deviation per patient was 33 ± 21, with counts ranging from 1 to 87.Taking account of all 288 large TP GS lesions, there were 191 (66.3%) lesions grouped as 'characteristic MS lesions', which were subdivided into three groups: 109 (57.1%) periventricular lesions, 44 (23.0%) infratentorial lesions, and 38 (19.9%) (juxta-)cortical lesions.All lesion counts, including TP UF lesions and all corresponding FN lesion counts, correlated with each other, are considered in the following Section.lesions, there were 191 (66.3%) lesions grouped as 'characteristic MS lesions', which were subdivided into three groups: 109 (57.1%) periventricular lesions, 44 (23.0%) infratentorial lesions, and 38 (19.9%) (juxta-)cortical lesions.All lesion counts, including TPUF lesions and all corresponding FN lesion counts, correlated with each other, are considered in the following Section.

FLAIRUF Compared with FLAIR3Da
Correlations between TPUF, FNUF, TP3Da, FN3Da, and TPGS lesions are schematically demonstrated in Figure 4. Lesion counts are specified in Figure 5, classified by size (large, small, wide, narrow).All counts of total TP and total FN lesions, as well as FP lesions, in both the FLAIRUF images and FLAIR3Da images, were analyzed, compared, and grouped by size.Correlations between TP UF , FN UF , TP 3Da , FN 3Da , and TP GS lesion counts.Schematic illustration (a) and contingency table (b).Note.TP GS = number of true-positive lesions detected using all contrasts available (gold standard); TP UF = true-positive lesions detected in the FLAIR UF images; FN UF = false-negative lesions using the FLAIR UF images; TP 3Da = true-positive lesions detected in the FLAIR 3Da images; FN 3Da = false-negative lesions using the FLAIR 3Da images.

FLAIRUF Compared with FLAIR3Da
Correlations between TPUF, FNUF, TP3Da, FN3Da, and TPGS lesions are schematically demonstrated in Figure 4. Lesion counts are specified in Figure 5, classified by size (large, small, wide, narrow).All counts of total TP and total FN lesions, as well as FP lesions, in both the FLAIRUF images and FLAIR3Da images, were analyzed, compared, and grouped by size.The sensitivity and PPV in terms of lesion detection using FLAIR UF compared with FLAIR 3Da are analyzed in Table 3 and visualized in Figure 6.For wide lesions, no statistically significant differences could be found in either sensitivity (p = 0.50) or PPV (overlap between confidence intervals).Neither was there a significant difference in PPV for narrow lesions (overlap between confidence intervals).Nevertheless, the sensitivity was significantly inferior in the FLAIR UF images for narrow large lesions (p < 0.001).For small lesions, the PPV was reduced in the FLAIR UF images compared with the FLAIR 3Da images (no overlap between confidence intervals), and the sensitivity was considerably lower in the FLAIR UF images compared with the FLAIR 3Da images (p < 0.001).Note.S = sequence; UF = FLAIR UF ; 3Da = FLAIR 3Da ; PPV = positive predictive value; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit; further abbreviations as in Figures 2-4.To compare sensitivity results (or, more precisely, to compare the FN UF and TP 3Da lesion counts with the FN 3Da and TP UF lesion counts) McNemar's test was performed, based on paired data shown in Figure 5.To compare PPV results, no statistical test was performed, since data were neither completely paired nor completely independent samples.
The sensitivity and PPV in terms of lesion detection using FLAIRUF compared with FLAIR3Da are analyzed in Table 3 and visualized in Figure 6.For wide lesions, no statistically significant differences could be found in either sensitivity (p = 0.50) or PPV (overlap between confidence intervals).Neither was there a significant difference in PPV for narrow lesions (overlap between confidence intervals).Nevertheless, the sensitivity was significantly inferior in the FLAIRUF images for narrow large lesions (p < 0.001).For small lesions, the PPV was reduced in the FLAIRUF images compared with the FLAIR3Da images (no overlap between confidence intervals), and the sensitivity was considerably lower in the FLAIRUF images compared with the FLAIR3Da images (p < 0.001).
. Sensitivity and PPV in terms of lesion detection, using FLAIRUF images (gray) and FLAIR3Da images (white).Four groups, which represent different lesion diameters, are displayed, respectively.The additional error bars denote the 95% CIs: (a) For wide lesions, no significant difference in sensitivity could be found.For the groups that comprise smaller lesions, however, the sensitivity was significantly inferior using the FLAIRUF images, decreasing more and more as a function of lesion diameter.(b) No significant differences in PPV were found for any of the large lesion groups (large, wide, and narrow).For small lesions, the PPV in the FLAIRUF images was moderately lower compared to the FLAIR3Da group (no overlap between confidence intervals).Note.Abbreviations as in Table 3. * p < 0.05.

Figure 6.
Sensitivity and PPV in terms of lesion detection, using FLAIR UF images (gray) and FLAIR 3Da images (white).Four groups, which represent different lesion diameters, are displayed, respectively.The additional error bars denote the 95% CIs: (a) For wide lesions, no significant difference in sensitivity could be found.For the groups that comprise smaller lesions, however, the sensitivity was significantly inferior using the FLAIR UF images, decreasing more and more as a function of lesion diameter.(b) No significant differences in PPV were found for any of the large lesion groups (large, wide, and narrow).For small lesions, the PPV in the FLAIR UF images was moderately lower compared to the FLAIR 3Da group (no overlap between confidence intervals).Note.Abbreviations as in Table 3. * p < 0.05.
Presumed causes of FN lesions in both image variants are quantified in Table 4 (large) and Table 5 (small).Presumed causes for lesions not being detected using FLAIR UF were (1) lesions were not visible owing to a combination of insufficient levels of spatial resolution, CNR, and SNR; (2) lesions were mistaken for cortex or other physiological structures owing to a combination of insufficient levels of spatial resolution, CNR, and SNR; and (3) lesions were concealed by distortion artifacts.Similarly, presumed causes for lesions not being detected using FLAIR 3Da were (1) lesions were mistaken for or masked by (mostly infratentorial) pulsation artifacts; (2) lesions were mistaken for cortex or other physiological structures; and (3) lesions were not visible at all, likely due to low resolution-related lesion contrast.Image examples of FN UF lesions and corresponding TP 3Da lesions are given in Figures 7 and 13c.Images of a FN 3Da lesion including the corresponding TP UF lesion are shown in Figure 15b.Note.SR = spatial resolution, esp.slice thickness; CNR = contrast-to-noise ratio; SNR = signal-to-noise ratio; further abbreviations as in Figures 2 and 4. 1 Cortex, in particular. 2 Causes of SR, CNR, and SNR could not strictly be distinguished.It is likely that there is a combination of those causes in most cases. 3All lesions were located in an infratentorial position.Note.Abbreviations as in Table 4. 1 Cortex, in particular. 2 Causes of SR, CNR, and SNR could not strictly be distinguished.It is likely that there is a combination of those causes in most cases. 3Three were located in an infratentorial position; the rest were located in a supratentorial position.
Frequent causes of FP lesions were in each case: partially imaged cortex or infratentorial pulsation artifacts appearing like lesions.While the former could be observed more frequently than the latter in the FLAIR UF images, they occurred at a similar rate in the FLAIR 3Da images.Comprehensive results are reported in Table 6.Image examples of FP UF and FP 3Da lesions are given in Figures 15 and 16e.Note.SR = spatial resolution, esp.slice thickness; FP UF = false-positive lesions using the FLAIR UF images; FP 3Da = false-positive lesions using the FLAIR 3Da images. 1 Cortex, in particular. 2 CNR and SNR are also partly involved. 3One was located in a supratentorial position; the rest were located in an infratentorial position. 4All lesions were located in an infratentorial position. 5Three were located in a supratentorial position; the rest were located in an infratentorial position.Note.SR = spatial resolution, esp.slice thickness; FPUF = false-positive lesions using the FLAIRUF images; FP3Da = false-positive lesions using the FLAIR3Da images. 1 Cortex, in particular. 2 CNR and SNR are also partly involved. 3One was located in a supratentorial position; the rest were located in an infratentorial position. 4All lesions were located in an infratentorial position. 5Three were located in a supratentorial position; the rest were located in an infratentorial position.

FLAIRUF Compared with FLAIRTSE
To compare FLAIRUF with FLAIRTSE images, all FLAIRTSE data available were utilized and matched with their FLAIRUF counterparts.TPTSE and FNTSE lesion counts added up to

FLAIR UF Compared with FLAIR TSE
To compare FLAIR UF with FLAIR TSE images, all FLAIR TSE data available were utilized and matched with their FLAIR UF counterparts.TP TSE and FN TSE lesion counts added up to the corresponding subset of TP GS lesion counts (tseTP GS ).Again, tseTP GS could be divided into corresponding subsets of TP UF and FN UF lesions each (tseTP UF and tseFN UF ).Correlations between tseTP UF , tseFN UF , TP TSE , FN TSE , and tseTP GS lesions are schematically demonstrated in Figure 8. Lesion counts are given in Figure 9, classified by size (large, small, wide, narrow).Image examples of TP lesions in FLAIR UF , FLAIR TSE , and FLAIR 3Da are shown side by side in Figure 10.FP TSE lesion counts along with corresponding tseFP UF counts can be found in Table 10.
The proportions of total TP lesions in both the FLAIR TSE and the corresponding FLAIR UF images are further analyzed in Table 7 and visualized in Figure 11, graded as to size.The sensitivity in terms of lesion detection did not differ significantly between the image variants in large (p = 0.68), wide (p > 0.99), or narrow (p > 0.99) lesion groups.For detecting small lesions, the sensitivity was lower using the FLAIR UF images; however, the difference could not be confirmed statistically (p = 0.08).
into corresponding subsets of TPUF and FNUF lesions each (tseTPUF and tseFNUF).Correlations between tseTPUF, tseFNUF, TPTSE, FNTSE, and tseTPGS lesions are schematically demonstrated in Figure 8. Lesion counts are given in Figure 9, classified by size (large, small, wide, narrow).Image examples of TP lesions in FLAIRUF, FLAIRTSE, and FLAIR3Da are shown side by side in Figure 10.FPTSE lesion counts along with corresponding tseFPUF counts can be found in Table 10.8. 1 One was FN using FLAIR3Da; the rest were TP in FLAIR3Da. 2Two were FN using FLAIR3Da; the rest were TP in FLAIR3Da. 3Three were FN using FLAIR3Da; the rest were TP in FLAIR3Da. 4Four were FN using FLAIR3Da; the rest were TP in FLAIR3Da.Lesion counts without superscripts were all TP in FLAIR3Da.the corresponding subset of TPGS lesion counts (tseTPGS).Again, tseTPGS could be divided into corresponding subsets of TPUF and FNUF lesions each (tseTPUF and tseFNUF).Correlations between tseTPUF, tseFNUF, TPTSE, FNTSE, and tseTPGS lesions are schematically demonstrated in Figure 8. Lesion counts are given in Figure 9, classified by size (large, small, wide, narrow).Image examples of TP lesions in FLAIRUF, FLAIRTSE, and FLAIR3Da are shown side by side in Figure 10.FPTSE lesion counts along with corresponding tseFPUF counts can be found in Table 10.
(a) (b)   8. 1 One was FN using FLAIR3Da; the rest were TP in FLAIR3Da. 2Two were FN using FLAIR3Da; the rest were TP in FLAIR3Da. 3Three were FN using FLAIR3Da; the rest were TP in FLAIR3Da. 4Four were FN using FLAIR3Da; the rest were TP in FLAIR3Da.Lesion counts without superscripts were all TP in FLAIR3Da.
Figure 9. Contingency tables including a subset of those TP GS lesions covered by the FLAIR TSE sequence (tseTP GS ).tseTP UF , tseFN UF , TP TSE , FN TSE , and tseTP GS lesion counts are correlated, and grouped by size, according to Figure 8. Note.Abbreviations as in Figure 8. 1 One was FN using FLAIR 3Da ; the rest were TP in FLAIR 3Da . 2 Two were FN using FLAIR 3Da ; the rest were TP in FLAIR 3Da . 3Three were FN using FLAIR 3Da ; the rest were TP in FLAIR 3Da . 4Four were FN using FLAIR 3Da ; the rest were TP in FLAIR 3Da .Lesion counts without superscripts were all TP in FLAIR 3Da .
Presumed causes of FN lesions in both image variants are quantified in Table 8 (large) and Table 9 (small).Causes for lesions not being detected appear to be relatively similar in the FLAIR UF and FLAIR TSE images: (1) lesions were not visible owing to resolutionassociated lesion contrast or did not sufficiently stand out from image noise; and (2) lesions were mistaken for cortex or other physiological structures.Table 10 summarizes the presumed causes of FP lesions.The proportions of total TP lesions in both the FLAIRTSE and the corresponding FLAIRUF images are further analyzed in Table 7 and visualized in Figure 11, graded as to size.The sensitivity in terms of lesion detection did not differ significantly between the image variants in large (p = 0.68), wide (p > 0.99), or narrow (p > 0.99) lesion groups.For detecting small lesions, the sensitivity was lower using the FLAIRUF images; however, the difference could not be confirmed statistically (p = 0.08).Note.TSE = FLAIRTSE; further abbreviations as in Table 3 and Figure 8.To compare sensitivity results, McNemar's test was performed, based on paired data shown in Figure 9. Unlike in the previous Section, the corresponding FP values (see Table 10) were insufficient to ensure reliable PPV analysis results; hence, it was omitted here.Note.Abbreviations as in Table 4 and Figure 8. 1 Cortex, in particular. 2 Causes of SR, CNR, and SNR could not strictly be distinguished.It is likely that there is a combination of those causes in most cases.

Figure 11.
Sensitivity in terms of lesion detection, using FLAIRTSE images (white) and corresponding FLAIRUF images (gray).Four groups, which represent different lesion diameters, are displayed, respectively.The additional error bars denote the 95% CIs.No significant differences in sensitivity could be detected for any of the lesion groups (p > 0.05).For small lesions, however, the data sugges a lower sensitivity using the FLAIRUF images compared to the FLAIRTSE images.Results imply that there is a correlation between lesion detectability and size in both cases, though.Note.Abbreviations as in Table 7.
Presumed causes of FN lesions in both image variants are quantified in Table 8 (large) and Table 9 (small).Causes for lesions not being detected appear to be relatively similar in the FLAIRUF and FLAIRTSE images: (1) lesions were not visible owing to resolution-associated lesion contrast or did not sufficiently stand out from image noise; and (2) lesions were mistaken for cortex or other physiological structures.Table 10 summarizes the presumed causes of FP lesions.Note.Abbreviations as in Table 4 and Figure 8. 1 Cortex, in particular. 2 Causes of SR, CNR, and SNR could not strictly be distinguished.It is likely that there is a combination of those causes in mos cases.Note.Abbreviations as in Table 4 and Figure 8. 1 Cortex, in particular. 2 Causes of SR, CNR, and SNR could not strictly be distinguished.It is likely that there is a combination of those causes in mos cases.
Figure 11.Sensitivity in terms of lesion detection, using FLAIR TSE images (white) and corresponding FLAIR UF images (gray).Four groups, which represent different lesion diameters, are displayed, respectively.The additional error bars denote the 95% CIs.No significant differences in sensitivity could be detected for any of the lesion groups (p > 0.05).For small lesions, however, the data suggest a lower sensitivity using the FLAIR UF images compared to the FLAIR TSE images.Results imply that there is a correlation between lesion detectability and size in both cases, though.Note.Abbreviations as in Table 7. Note.Abbreviations as in Table 4 and Figure 8. 1 Cortex, in particular. 2 Causes of SR, CNR, and SNR could not strictly be distinguished.It is likely that there is a combination of those causes in most cases.Note.SR = spatial resolution, esp.slice thickness; FP TSE = false-positive lesions using the FLAIR TSE images; tseFP UF = corresponding subset of FP UF recorded with FLAIR TSE . 1 Cortex, in particular. 2 CNR and SNR are also partly involved.

Dependence on Size and Location within FLAIR UF
Unlike in Sections 3.2.1 and 3.2.2, the results of ordinal lesion conspicuity ratings of the FLAIR UF images, based on the FLAIR 3Da images, are presented in this Section.Comprehensive conspicuity ratings are given in Table 11 and visualized in Figure 12, grouped by lesion size and brain regions.As alluded to in Section 3.2.1,there is a statistically significant difference in terms of lesion conspicuity between large and small FLAIR UF lesions (p < 0.001).Testing for conspicuity differences between frontal, parietal, temporal, occipital, central, and infratentorial lesions was significant within the large lesion group (p = 0.002), but not among small lesions (p = 0.15).Further analysis of large lesions revealed that the differences could be attributed to occipital and central lesions.The conspicuity of large lesions among all the other regions did not differ significantly (p = 0.42).Occipital large lesions proved to be more conspicuous (p = 0.002) and central large lesions proved to be less conspicuous (p = 0.01) compared to all the other large lesions (frontal, parietal, temporal, and infratentorial).Note.TP 3Da = true-positive lesion counts using FLAIR 3Da .The counts of lesions are given.The conspicuity was rated on an ordinal five-point Likert scale, based on the FLAIR 3Da images: 1 = better/larger in the FLAIR UF images; 2 = equal; 3 = better in the FLAIR 3Da images but classified as a lesion using only the FLAIR UF images; 4 = better in the FLAIR 3Da images and classified as no lesion using only the FLAIR UF images; 5 = FLAIR 3Da lesion that is not visible at all in the FLAIR UF images. 1 Insular lobe, corpus callosum, basal nuclei, and diencephalon.
Nevertheless, the results are only valid if they are not biased by other parameters, particularly lesion size variations among the brain regions.Therefore, the average lengthwise lesion diameters are compared in Table 12.The results show that there is no significant or relevant deviation of any group, either for large lesions (p = 0.26) or small lesions (p = 0.06).Moreover, Table 13 demonstrates the distribution of large lesions between wide and narrow lesions, clustered by location.Also, each group is differentiated by conspicuity ratings.In comparison to all the other locations, there were two brain regions where there were relatively more wide lesions than narrow lesions: occipital and infratentorial.The conspicuity ratings of wide infratentorial lesions appear to be better than the conspicuity ratings of narrow infratentorial lesions; however, that did not hold true for occipital lesions (good ratings without exception).Lesion conspicuity was significantly superior for large lesions compared to small lesions.Unless for small lesions, there was a significant difference among some brain regions for large lesions.This could be attributed to occipital lesions (superior) and central lesions (inferior).Note.Black = 1; dark gray = 2; gray = 3; light gray = 4; white = 5.Abbreviations and brain regions as in Table 11.* p < 0.05.Lesion conspicuity was significantly superior for large lesions compared to small lesions.Unless for small lesions, there was a significant difference among some brain regions for large lesions.This could be attributed to occipital lesions (superior) and central lesions (inferior).Note.Black = 1; dark gray = 2; gray = 3; light gray = 4; white = 5.Abbreviations and brain regions as in Table 11.* p < 0.05.Note.M = mean; CI = confidence interval; LL = lower limit; UL = upper limit; SD = standard deviation; Mdn = median; further abbreviations and brain regions as in Table 11.Since the assumptions of normal distribution of the data were not met, a Kruskal-Wallis test was performed.SNR and CNR in the FLAIR UF images were rated significantly inferior compared to the FLAIR 3Da images (p < 0.001 each).Rating results are given in Table 14.They represent the overall judgments of the slice series.In fact, there were unequal SNR and CNR levels among the slices, and there was also a positional dependence within one image slice.This is further examined in Section 3.3.3.For image examples, see Figure 13.Table 15 lists all artifacts that could be identified in each sequence variant and shows detailed results.Image series were rated positive for artifacts in cases where the artifact could be detected in at least one image slice.First of all, spatial distortions occurred in each of the FLAIRUF sequences, and did not appear in any of the FLAIR3D sequences, representing the main difference.Regions of distortions were as follows: frontal, frontobasal, temporopolar, and infratentorial.As an example, see the images of spatial distortions Table 15 lists all artifacts that could be identified in each sequence variant and shows detailed results.Image series were rated positive for artifacts in cases where the artifact could be detected in at least one image slice.First of all, spatial distortions occurred in each of the FLAIR UF sequences, and did not appear in any of the FLAIR 3D sequences, representing the main difference.Regions of distortions were as follows: frontal, frontobasal, temporopolar, and infratentorial.As an example, see the images of spatial distortions (Figure 14) as well as the image of a temporopolar distortion-related FN lesion in Figure 7d.Furthermore, we observed infratentorial pulsatile flow artifacts in all sequences, in both the FLAIR UF images and the FLAIR 3Da images.Pulsation artifacts (infratentorial and supratentorial) in the FLAIR UF images typically appeared as hyperintense or hypointense dots, spots, or streaks.Pulsation artifacts in the FLAIR 3Da images, however, typically appeared as grainy, hyperintense artifact bands positioned at the level of the pons.Limitation of diagnostic information was assumed if there was a tendency or possibility of artifacts mimicking or masking lesions.The former mainly applied to the FLAIR UF images, and the latter mainly pertained to the FLAIR 3Da images.Infratentorial pulsation artifacts and related limitations of diagnostic information are demonstrated in Figure 15.Aside from that, we found other infrequent minor artifacts that almost do not affect diagnostic information and occur only in the FLAIR UF images: supratentorial pulsation artifacts, frontal chemical shift artifacts due to incomplete fat suppression, central residual aliasing, and spike artifacts.Plus, there were some motion artifacts in one FLAIR 3Da image series that could not be seen in the corresponding FLAIR UF images.Image examples of those minor artifacts are presented in Figure 16.Finally, it must be pointed out that all analyses were confined to the brain parenchyma.The subarachnoid space, the dura mater, the cranium, and the scalp, along with other parts of the head such as the nasal cavity and the paranasal sinuses, the internal and external ear, the oral cavity, the throat, the eyes, the orbits, the cranial nerves, muscles, adipose tissue, and other osseous or chondral structures, are insufficiently delineated in the FLAIRUF images, as can be seen in the image examples.Finally, it must be pointed out that all analyses were confined to the brain parenchyma.The subarachnoid space, the dura mater, the cranium, and the scalp, along with other parts of the head such as the nasal cavity and the paranasal sinuses, the internal and external ear, the oral cavity, the throat, the eyes, the orbits, the cranial nerves, muscles, adipose tissue, and other osseous or chondral structures, are insufficiently delineated in the FLAIR UF images, as can be seen in the image examples.

FLAIR UF Compared with FLAIR TSE
SNR and CNR were rated relatively similar in the FLAIR UF and FLAIR TSE images (see Table 16).However, the low number of paired slice series only serves to provide a rough orientation.Results are overall ratings of the slice series, and positional dependences of SNR and CNR (as demonstrated in Section 3.3.3)seem to be similar in the FLAIR UF and FLAIR TSE images.Artifacts in the FLAIR UF and FLAIR TSE images are contrasted in Table 17.Again, the small amount of data provides only a rough impression.FLAIR UF results are specified in greater detail in Section 3.3.1.Relevant artifacts in the FLAIR TSE images that have the potential to significantly limit diagnostic information are infratentorial pulsatile flow artifacts, either by masking or mimicking lesions.Typically, they appeared as a streaky, irregular (hyperintense and hypointense) band across the middle of the cerebellum, between the right and left sigmoid sinuses (see Figure 17).

Positional Dependence of SNR and CNR in FLAIR UF
In the FLAIR UF images, the vicinity of each TP GS lesion was rated for relative SNR and CNR levels (substandard or standard/superior; with reference to the average FLAIR UF image quality).Tables 18 and 19 present the results grouped by brain regions.Within a region, lesion-rich areas are given more weight than lesion-poor areas.Compared to all the other brain regions, the SNR appeared significantly reduced in central and infratentorial brain regions (p < 0.001), and the CNR appeared significantly lower in infratentorial brain regions (p < 0.001).What is not evident from the data and is a distinctive characteristic of the infratentorial region is the highly uneven distribution of SNR levels across this area: from highly substandard (in central areas such as the pons) to highly above average (in peripheral areas such as the posterior lobe of the cerebellum).The CNR, however, appeared to be reduced all over the infratentorial areas; particularly, the fine folium-sulcus texture of the cerebellum could not be distinguished sufficiently.Image examples are given in Figure 18. the infratentorial region is the highly uneven distribution of SNR levels across this area: from highly substandard (in central areas such as the pons) to highly above average (in peripheral areas such as the posterior lobe of the cerebellum).The CNR, however, appeared to be reduced all over the infratentorial areas; particularly, the fine folium-sulcus texture of the cerebellum could not be distinguished sufficiently.Image examples are given in Figure 18.Note.SNR = signal-to-noise ratio; TPGS = true-positive lesion counts using the gold standard; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.Note.CNR = contrast-to-noise ratio; further abbreviations as in Table 18.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.

<0.001
Diagnostics 2024, 14, x FOR PEER REVIEW 25 of 35 the infratentorial region is the highly uneven distribution of SNR levels across this area: from highly substandard (in central areas such as the pons) to highly above average (in peripheral areas such as the posterior lobe of the cerebellum).The CNR, however, appeared to be reduced all over the infratentorial areas; particularly, the fine folium-sulcus texture of the cerebellum could not be distinguished sufficiently.Image examples are given in Figure 18.Note.SNR = signal-to-noise ratio; TPGS = true-positive lesion counts using the gold standard; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.the infratentorial region is the highly uneven distribution of SNR levels across this area: from highly substandard (in central areas such as the pons) to highly above average (in peripheral areas such as the posterior lobe of the cerebellum).The CNR, however, appeared to be reduced all over the infratentorial areas; particularly, the fine folium-sulcus texture of the cerebellum could not be distinguished sufficiently.Image examples are given in Figure 18.Note.SNR = signal-to-noise ratio; TPGS = true-positive lesion counts using the gold standard; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.the infratentorial region is the highly uneven distribution of SNR levels across this area: from highly substandard (in central areas such as the pons) to highly above average (in peripheral areas such as the posterior lobe of the cerebellum).The CNR, however, appeared to be reduced all over the infratentorial areas; particularly, the fine folium-sulcus texture of the cerebellum could not be distinguished sufficiently.Image examples are given in Figure 18.Note.SNR = signal-to-noise ratio; TPGS = true-positive lesion counts using the gold standard; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.Note.SNR = signal-to-noise ratio; TP GS = true-positive lesion counts using the gold standard; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.the infratentorial region is the highly uneven distribution of SNR levels across this area: from highly substandard (in central areas such as the pons) to highly above average (in peripheral areas such as the posterior lobe of the cerebellum).The CNR, however, appeared to be reduced all over the infratentorial areas; particularly, the fine folium-sulcus texture of the cerebellum could not be distinguished sufficiently.Image examples are given in Figure 18.Note.SNR = signal-to-noise ratio; TPGS = true-positive lesion counts using the gold standard; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.Note.CNR = contrast-to-noise ratio; further abbreviations as in Table 18.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.

<0.001
Diagnostics 2024, 14, x FOR PEER REVIEW 25 of 35 the infratentorial region is the highly uneven distribution of SNR levels across this area: from highly substandard (in central areas such as the pons) to highly above average (in peripheral areas such as the posterior lobe of the cerebellum).The CNR, however, appeared to be reduced all over the infratentorial areas; particularly, the fine folium-sulcus texture of the cerebellum could not be distinguished sufficiently.Image examples are given in Figure 18.Note.SNR = signal-to-noise ratio; TPGS = true-positive lesion counts using the gold standard; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.the infratentorial region is the highly uneven distribution of SNR levels across this area: from highly substandard (in central areas such as the pons) to highly above average (in peripheral areas such as the posterior lobe of the cerebellum).The CNR, however, appeared to be reduced all over the infratentorial areas; particularly, the fine folium-sulcus texture of the cerebellum could not be distinguished sufficiently.Image examples are given in Figure 18.Note.SNR = signal-to-noise ratio; TPGS = true-positive lesion counts using the gold standard; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.Note.CNR = contrast-to-noise ratio; further abbreviations as in Table 18.Brain regions as in Table 11.The p-values were calculated using the chi-squared test.The SNR is substandard around the pons and improves exceedingly toward the posterior lobe of the cerebellum to an excellent level.The CNR, however, seems generally slightly substandard in this area compared to other brain regions, even in the most posterior regions.This may be associated with the characteristic, fine folium-sulcus texture of the cerebellum (e.g., vermis or posterior lobe), which cannot be distinguished clearly.

Main Findings of This Study
This study investigated an ultrafast axial DL-enhanced EPI-FLAIR (FLAIRUF) MRI sequence in RRMS patients.The FLAIRUF sequence reduced the acquisition time to almost a sixth of the time required for conventional three-dimensional FLAIR (FLAIR3D) acquisitions and nearly a third of the time required for axial standard TSE-FLAIR (FLAIRTSE) acquisitions.Based on specific parameters, we investigated lesion detection and image quality in the three sequences.With regard to the detection of inflammatory brain lesions, we did not observe any significant differences between the FLAIRUF images and the FLAIR3Da images in either sensitivity for wide large lesions (≥3 mm × ≥2 mm) or PPV for all large lesions (≥3 mm total) despite the high lesion numbers.The same applies to the sensitivity of large lesion detection (≥3 mm total) in the FLAIRUF images compared to the FLAIRTSE images.Lesion conspicuity did not differ significantly between brain regions in the FLAIRUF images, except for occipital large lesions (superior results) and central large lesions (inferior results).SNR and CNR were significantly inferior using FLAIRUF compared to FLAIR3D, which particularly applied to specific areas of the image.In contrast, SNR and CNR in FLAIRUF appeared to be closely comparable to FLAIRTSE.Unlike FLAIRTSE and FLAIR3D, the image quality in FLAIRUF was limited by distortion artifacts in typical The SNR is substandard around the pons and improves exceedingly toward the posterior lobe of the cerebellum to an excellent level.The CNR, however, seems generally slightly substandard in this area compared to other brain regions, even in the most posterior regions.This may be associated with the characteristic, fine folium-sulcus texture of the cerebellum (e.g., vermis or posterior lobe), which cannot be distinguished clearly.

Main Findings of This Study
This study investigated an ultrafast axial DL-enhanced EPI-FLAIR (FLAIR UF ) MRI sequence in RRMS patients.The FLAIR UF sequence reduced the acquisition time to almost a sixth of the time required for conventional three-dimensional FLAIR (FLAIR 3D ) acquisitions and nearly a third of the time required for axial standard TSE-FLAIR (FLAIR TSE ) acquisitions.Based on specific parameters, we investigated lesion detection and image quality in the three sequences.With regard to the detection of inflammatory brain lesions, we did not observe any significant differences between the FLAIR UF images and the FLAIR 3Da images in either sensitivity for wide large lesions (≥3 mm × ≥2 mm) or PPV for all large lesions (≥3 mm total) despite the high lesion numbers.The same applies to the sensitivity of large lesion detection (≥3 mm total) in the FLAIR UF images compared to the FLAIR TSE images.Lesion conspicuity did not differ significantly between brain regions in the FLAIR UF images, except for occipital large lesions (superior results) and central large lesions (inferior results).SNR and CNR were significantly inferior using FLAIR UF compared to FLAIR 3D , which particularly applied to specific areas of the image.In contrast, SNR and CNR in FLAIR UF appeared to be closely comparable to FLAIR TSE .Unlike FLAIR TSE and FLAIR 3D , the image quality in FLAIR UF was limited by distortion artifacts in typical locations.To our knowledge, this study represents the first in-depth quantitative and qualitative evaluation of inflammatory lesions using a DL-enhanced EPI-FLAIR sequence.

Significance of MRI and Ultrafast MRI
In MS patients, the severity of the clinical course is influenced by the start and choice of disease-modifying therapy (DMT).Notably, early treatment start or treatment escalation delays disease progress of both clinically definite multiple sclerosis (CDMS) and clinically isolated syndrome (CIS) in particular [67].To decide on adequate therapeutic strategies depending on disease activity, MRI plays a major role [10,67,68].McDonald diagnostic criteria are based on number, location, and size of lesions as well as temporal changes in lesions [6][7][8][9].A lesion size of over 3 mm is deemed to be appropriate to account for the diagnostic criteria [14].In order to adapt DMT without delay, specific monitoring intervals are recommended [10].Notably, CIS patients benefit from very tight follow-up MRI examinations, particularly during the first year [10,69].Nonetheless, overall MRI scan capacity is far from enough to satisfy demand in numerous parts of the world, and waiting times usually exceed the maximum recommended time, thus worsening medical outcomes and increasing the economic costs [70][71][72][73][74][75].Against this background, utilizing DL-enhanced EPI-FLAIR sequences may be a promising approach to ameliorate treatment quality while at the same time meeting diagnostic requirements.It might help facilitate low-threshold access to MRI examinations at an earlier stage and at shorter intervals before MS has been diagnosed.Whether FLAIR UF or related techniques could actually be suitable for monitoring the early stages of the disease still requires further investigation.

Limitations of the FLAIR UF Images
Despite its remarkable capabilities, the FLAIR UF images still have three primary limitations.First, lesion size is a limiting factor for the FLAIR UF images, particularly in comparison with the FLAIR 3Da images.Almost every second small lesion (<3 mm) and more than every fifth narrow large lesion (≥3 mm × <2 mm) could not be detected using the FLAIR UF images.Additionally, out of all presumed small lesions (<3 mm), about a quarter detected using the ultrafast sequence variant were false-positives.There may be one main cause of these limitations: the relatively large slice thickness including slice gaps in this context.It should be noted that this parameter even influenced the detection of large lesions (≥3 mm).
Secondly, lesion location may represent another limiting factor in the FLAIR UF images.There are several location-dependent factors: (1) SNR deterioration toward the central regions of the images, and simultaneously, SNR enhancement toward marginal regions such as the occipital lobe (even though it appears similar compared with the FLAIR TSE images); (2) some limits of CNR, which primarily become apparent when depicting regions that require high spatial resolution and low slice thickness, particularly the cerebellum with its fine folium-sulcus texture (even though the overall CNR only seems marginally reduced compared with the FLAIR TSE images); (3) pulsatile flow artifacts, which mostly affect image quality by mimicking infratentorial lesions (even though infratentorial pulsation artifacts appear to limit image quality to a similar extent using FLAIR 3Da or FLAIR TSE , mostly by masking potential lesions); (4) spatial distortion artifacts (frontal, frontobasal, temporopolar, and infratentorial), which may conceal potential lesions; and (5) relatively large slice thickness including slice gaps, which may hinder the distinction between lesions and physiological brain structures at certain locations, particularly near the cortex.
Thirdly, there is an additional limitation apart from those associated with lesion assessment: most structures outside the parenchyma are insufficiently delineated in the FLAIR UF images, such as osseus structures, air-containing structures, or adipose tissue.Reasons for this may be susceptibility artifacts and fat suppression.However, the optic nerves, for example, are clearly distinguishable, and fat suppression might even be advantageous in terms of assessing optic neuritis.However, this remains speculative and beyond the scope of our study.

Considerations on Ratings for Lesion Conspicuity in FLAIR UF
Ratings for lesion conspicuity in FLAIR UF (Section 3.2.3)were intended to compare results among the brain regions within the FLAIR UF images.Since a kind of reference was required (FLAIR 3Da ), the ratings appear to be biased by position-dependent lesion conspicuity in FLAIR 3Da .However, those potential position-dependent differences did not exist at all in the FLAIR 3Da images, and image quality was excellent across all regions.The only exception was infratentorial, where a few lesions were masked by pulsation artifacts (FN lesions); all the other infratentorial lesions were not affected in any way.In order to prevent FLAIR 3Da -biased (infratentorial) conspicuity ratings in FLAIR UF , assessments were confined to TP 3Da lesions instead of TP GS lesions.
As stated previously, the conspicuity ratings do not seem to be biased by lesion size variations among the brain regions, with one exception: ratings of infratentorial large lesions might have been biased toward falsely positive results, since the proportion of wide lesions to narrow lesions was greater compared to all the other regions, while at the same time, the conspicuity ratings appeared to be superior for wide lesions.That might be an explanation for why the conspicuity ratings do not reflect the substandard SNR and CNR ratings for this region; this discrepancy did not exist in other locations.
Finally, localization-dependent conspicuity results in FLAIR UF could only be demonstrated for large lesions, not for small lesions.The underlying cause could be that slice thickness and slice gaps are the limiting factors for small lesion conspicuity, which is independent of the position.SNR-related positional dependence appears to have a subordinate role in this context.For instance, large central lesions stood out with a comparatively high proportion of grade 3 ratings (conspicuity better in the FLAIR 3Da images but classified as a lesion using only the FLAIR UF images), but for small central lesions, that proportion was relatively low in favor of grade 4 and 5 ratings (not classified as a lesion using FLAIR UF ).In all the other brain regions, on the contrary, large lesions predominantly exhibited grade 1 and 2 ratings (conspicuity at least equal in the FLAIR UF images), i.e., comparatively small proportions of grade 3 ratings, while for small lesions, that proportion was considerably lower in favor of grade 4 and 5 ratings, comparable to the central small lesion group.However, for occipital and infratentorial small lesions, the counts were insufficient to draw further conclusions regarding the position-dependent conspicuity of small lesions.

Outcomes Correlated with Technical Features
The above-mentioned characteristics of the FLAIR UF images arise from specific sequence properties.To begin with, there is a correlation between slice thickness (slice gap included) and visibility of small lesions: if the lesion size is smaller than the slice thickness, the signal intensity will decrease, and with it the lesion contrast due to the partial volume effect.At some point, the lesion signal will disappear completely if the lesions are too small.Thus, there is a direct correlation between the threshold and slice thickness.
Apart from the partial volume effect, EPI-related CNR loss seems to be sufficiently counterbalanced by MT preparation [62].Here, MT pulses serve as saturation pulses selectively affecting macromolecular protons and adjacent water molecules to enhance image contrast [76].
In EPI, the SNR is diminished due to substantial signal loss during its long echo train, and lack of refocusing pulses combined with rapid T2* signal decay.Nevertheless, aided by the DL reconstruction, the SNR in FLAIR UF appeared similar compared to FLAIR TSE .In both image variants, the SNR (and related lesion conspicuity) decreased similarly toward the center of the image, owing to coil dependency in PI [77][78][79].Accordingly, there is an inversely proportional relationship between SNR and the geometry (g)-factor [77][78][79].This factor is affected by coil sensitivity and voxel location, usually exhibiting the greatest values in the center of the image [77][78][79].Although the DL reconstruction method was designed to compensate for this specific factor, its SNR gains were eventually limited, especially in combination with stringent requirements for slice thickness.In contrast, the excellent SNR in FLAIR 3D can be attributed to the fact that in 3D acquisitions, the complete volume is excited with each shot.
In addition, the long readout duration in EPI has another significant impact on image quality (together with the very low pixel bandwidth along the phase-encoding direction in EPI): increased sensitivity to susceptibility artifacts [80].If a substance is located in an external homogeneous magnetic field, the magnetic field lines will either be dispersed (e.g., bone tissue) or concentrated (e.g., air or metal) around that material, depending on its susceptibility properties [81].Consequently, the magnetic field is disturbed in areas where susceptibility differences are large (e.g., air-filled bones), leading to an accelerated phase coherence loss and T2* signal decay, on the one hand, and accumulation of phase errors along with positioning errors in the phase encoding direction, on the other [2].This explains the appearance of both distortion artifacts, particularly arising in the vicinity of air-tissue interfaces in the phase encoding direction, and signal decrease in tissues surrounding the brain parenchyma, caused by differences in magnetic susceptibility.In strong magnetic fields, susceptibility differences further increase, and phase incoherences accumulate to a greater extent over the duration of the echo train [2,82,83].In contrast, SNR, CNR, spatial resolution, and scan time benefit from a strong magnetic field strength [82,83].
Apart from susceptibility effects, there is another reason for the signal decrease in non-parenchymal tissues in FLAIR UF : fat suppression.It is essential for low-segmented EPI in order to avert chemical shift artifacts [2].Those artifacts derive from spatial signal misregistration owing to frequency differences between protons in fat and water.Without fat suppression, however, the implicitly low pixel bandwidth along the phase-encoding direction in EPI would lead to the accumulation of phase shifts between fat and water, resulting in chemical shift artifacts in the phase-encoding direction [80,84].With singular exceptions located close to the frontal air-tissue interfaces, we did not observe any chemical shift artifacts caused by incomplete fat suppression in the FLAIR UF images.
In addition, we observed other artifacts caused by phase errors: pulsatile flow artifacts.They usually occur along the phase encoding direction [85].Accordingly, we were able to relate most artifacts to structures containing flowing blood or liquor in all three image variants (FLAIR UF , FLAIR TSE , and FLAIR 3Da ), each of them revealing distinctive characteristics.Typically, the artifacts either occurred around blood vessels, particularly around infratentorial venous sinuses (FLAIR UF ), or they were aligned in positions shifted from blood vessels along the phase encoding direction (FLAIR UF , FLAIR 3Da , and FLAIR TSE ).In the FLAIR UF and FLAIR 3Da images, artifacts were shifted along the anterior-posterior axis, while in the FLAIR TSE images, artifacts were shifted along the left-right axis (compare Table 2).In the FLAIR UF images, replicas were shifted at intervals of a quarter of the field of view (according to the PI acceleration factor of two and the two-segmented k-space).In the FLAIR 3Da and FLAIR TSE images, artifacts manifested as grainy or streaky bands.However, in terms of limitations of diagnostic information caused by pulsation artifacts, the FLAIR UF images were not inferior.
Finally, there were three more rare minor artifacts, two of which could be seen in the FLAIR UF images: residual aliasing and spike artifacts.The former is related to PI reconstruction, and the latter is related to the rapid switching of gradients in EPI.The third artifact type, minor motion artifacts caused by subject movement, could not be observed in the FLAIR UF images, but rather in one FLAIR 3Da and one FLAIR TSE image series.This is related to the longer acquisition time in FLAIR 3D and FLAIR TSE .In this regard, EPI is undeniably at an advantage.

Limitations of the Study
There are some limitations of this study that might have affected our results.To begin with, the methodological approach may be associated with some selection bias.Examinations were carried out using one specific scanner, and the patient sample may be biased by common appointment scheduling practices in terms of disease manifestation or severity (medium or low disease activity in an outpatient follow-up setting).Although no patient declined to participate in the study, there were a couple of patients who could not be included from the outset due to the associated additional expenditure of time.That probably led to further sampling bias (e.g., age, sex, clinical condition).In particular, the sex ratio in our study was atypical (compare Sections 1 and 2.1).Aside from selection bias, there were more factors limiting the external validity, such as the single-center study design using only one single 3 T MRI scanner from one specific manufacturer.In consideration of our methodological objective to analyze lesion detection and image quality in general, it must be pointed out that the comparisons performed could only be based on limited parameters.
Moreover, there might have been some observer bias inherent to this study, despite all efforts and critical appraisal.In this regard, implementing image blinding would not have had a positive effect due to the significantly different and easily distinguishable nature of the images, and the study was designed for subjective side-by-side comparisons.Generally, radiological readers' assessments may also depend on their level of experience with the FLAIR UF image characteristics.
Even though the number of participants was modest, the lesion counts were substantial, providing high statistical power.Nevertheless, there is an important limitation from a statistical point of view: statistically, several lesions within one subject are not independent observations, because they are bound to both the same individual and time of scan.Accordingly, lesion conspicuity could possibly have been influenced by parameters that might have varied among the individuals or scans, which may have induced statistical bias.Also, non-significant test results are indications, but not proof, of equality.So, in large part, the results of the study are indicative, not conclusive.However, variances among the image data appeared to play a minor role, at most, and non-significant results are more meaningful when based on a large sample size.Methodologically, it would have been precise either to rate all lesions from one image series as a whole or to randomly select and analyze only one lesion from each image series after recording all existing lesions.However, the former procedure would have provided highly inaccurate outcomes, and the latter procedure would have provided relatively meaningless test results (if statistically non-significant), owing to the sample size.In this study, the small FLAIR TSE sample size only provides indicative findings: results were either statistically tested, accepting the possibility of an increased type II error, or statistical testing was omitted.
While the acquisition sequence used in this study would allow for reducing the slice thickness to 3 mm without compromising scan coverage and total scan time, it remains unclear to what extent DL processing may compensate for the corresponding 25% reduction in SNR.Assessing further optimizations of the acquisition protocol may represent a rewarding task for future studies.Also, further prospective studies and confirmatory trials with larger numbers of participants and more diverse cohorts, various scanners, and modified reconstruction and acquisition methods need to be conducted.Further radiological reading should possibly include separate image assessments instead of side-by-side comparisons.Plus, other studies covering MS patients might consider additional contrasts (e.g., contrast-enhanced T1w sequences), orientations (sagittal, coronal), or regions (such as the spinal cord or the optic nerves).

Future Perspectives
All in all, our results suggest that FLAIR UF may be an appropriate approach for assessing cerebral inflammatory lesions.Diagnostic performance did not prove inferior compared to conventional FLAIR 3D in terms of MS lesion detection (≥3 mm × ≥2 mm) in just about a sixth of the time.This demonstrates the tremendous progress made in the field of EPI.In light of extensive waiting times for MRI examinations and patient-dependent constraints to carry them out, for example, claustrophobia or inability to remain motionless (i.e., children or disease-and age-related obstacles), ultrafast imaging seems an essential tool.However, there are still some challenges to overcome in refining this technique while aiming to combine high resolution with high SNR at high field strengths along with low artifacts.

Conclusions
In conclusion, this study indicates that ultrafast DL-enhanced EPI-FLAIR might be an appropriate method for the assessment of cerebral inflammatory lesions (≥3 mm) in MS.Apart from relatively minor limitations regarding lesion size and lesion location, it did not prove inferior to conventional FLAIR methods in the main, while being conducted in a fraction of time.In consideration of both MRI waiting times and patients who cannot remain motionless in the scanner for long, EPI might be an expedient alternative to conventional imaging.However, further studies are required to confirm our findings.

Figure 2 .
Figure2.Classification of TP GS lesions.Note.TP GS lesions = total number of true-positive lesions detected using all contrasts available (gold standard); FLAIR 3Da = axial reconstruction of FLAIR 3D ; FLAIR UF = ultrafast axial FLAIR; tseTP GS = subset of TP GS recorded with FLAIR TSE ; FLAIR TSE = axial standard TSE-FLAIR; GS = utilization of all contrasts available, particularly T2-FLAIR, T1, and T2; TP = true-positives; FN = false-negatives; 1 = better/larger in the FLAIR UF images compared to FLAIR 3Da ; 2 = equal compared to FLAIR 3Da ; 3 = better in the FLAIR 3Da images, but classified as a lesion using only the FLAIR UF images; 4 = better in the FLAIR 3Da images and classified as no lesion using only the FLAIR UF images; 5 = FLAIR 3Da lesion that is not at all visible in the FLAIR UF images; PV = periventricular; (J-)C = (juxta-)cortical; I = infratentorial (brainstem, cerebellum); F = frontal; P = parietal; T = temporal; O = occipital; C = central (insular lobe, corpus callosum, basal nuclei, diencephalon).

Figure 4 .
Figure 4.Correlations between TP UF , FN UF , TP 3Da , FN 3Da , and TP GS lesion counts.Schematic illustration (a) and contingency table (b).Note.TP GS = number of true-positive lesions detected using all contrasts available (gold standard); TP UF = true-positive lesions detected in the FLAIR UF images; FN UF = false-negative lesions using the FLAIR UF images; TP 3Da = true-positive lesions detected in the FLAIR 3Da images; FN 3Da = false-negative lesions using the FLAIR 3Da images.
Note. S = sequence; UF = FLAIRUF; 3Da = FLAIR3Da; PPV = positive predictive value; CI = confidence interval (Clopper-Pearson); LL = lower limit; UL = upper limit; further abbreviations as in Figures2-4.To compare sensitivity results (or, more precisely, to compare the FNUF and TP3Da lesion counts with the FN3Da and TPUF lesion counts) McNemar's test was performed, based on paired data shown in Figure5.To compare PPV results, no statistical test was performed, since data were neither completely paired nor completely independent samples.

Figure 7 .
Figure 7. FNUF lesions (top row) contrasted with their TP3Da counterparts (arrow, bottom row).The images also show several other lesions: (a) Lesion in the splenium of the corpus callosum (approx.3 mm × 2 mm).Causes for it not being detected may be image noise and slice thickness/slice gaps.(b) Left frontal lesion (approx.3 mm × 1 mm).Causes may be associated with slice thickness/contrast as well as image noise.(c) Thin right frontal lesion (approx.7 mm × 1 mm).It was mistaken for cortex in the FLAIRUF image.(d) Left temporopolar lesion (approx.3 mm × 2 mm).It was not recognized as such in the FLAIRUF image owing to commonly occurring distortions within this region.Note.Corresponding slices could not be positioned exactly identically for two reasons: Different slice thicknesses including slice gaps (c) and non-parallel slice inclinations (d).

Figure 7 .
Figure 7. FN UF lesions (top row) contrasted with their TP 3Da counterparts (arrow, bottom row).The images also show several other lesions: (a) Lesion in the splenium of the corpus callosum (approx.3 mm × 2 mm).Causes for it not being detected may be image noise and slice thickness/slice gaps.(b) Left frontal lesion (approx.3 mm × 1 mm).Causes may be associated with slice thickness/contrast as well as image noise.(c) Thin right frontal lesion (approx.7 mm × 1 mm).It was mistaken for cortex in the FLAIR UF image.(d) Left temporopolar lesion (approx.3 mm × 2 mm).It was not recognized as such in the FLAIR UF image owing to commonly occurring distortions within this region.Note.Corresponding slices could not be positioned exactly identically for two reasons: Different slice thicknesses including slice gaps (c) and non-parallel slice inclinations (d).

Figure 9 .
Figure9.Contingency tables including a subset of those TPGS lesions covered by the FLAIRTSE sequence (tseTPGS).tseTPUF, tseFNUF, TPTSE, FNTSE, and tseTPGS lesion counts are correlated, and grouped by size, according to Figure8.Note.Abbreviations as in Figure8.1 One was FN using FLAIR3Da; the rest were TP in FLAIR3Da.2Two were FN using FLAIR3Da; the rest were TP in FLAIR3Da.3Three were FN using FLAIR3Da; the rest were TP in FLAIR3Da.4Four were FN using FLAIR3Da; the rest were TP in FLAIR3Da.Lesion counts without superscripts were all TP in FLAIR3Da.

Figure 8 .
Figure 8. Correlations between tseTP UF , tseFN UF , TP TSE , FN TSE , and tseTP GS lesion counts.Schematic illustration (a) and contingency table (b).Note.TP GS = number of true-positive lesions detected using all contrasts available (gold standard); tseTP GS = subset of TP GS recorded with FLAIR TSE ; tseTP UF = corresponding subset of TP UF recorded with FLAIR TSE ; tseFN UF = corresponding subset of FN UF recorded with FLAIR TSE ; TP TSE = true-positive lesions detected in the FLAIR TSE images; FN TSE = false-negative lesions using the FLAIR TSE images; further abbreviations as in Figure 4.

Figure 9 .
Figure 9. Contingency tables including a subset of those TPGS lesions covered by the FLAIRTSE sequence (tseTPGS).tseTPUF, tseFNUF, TPTSE, FNTSE, and tseTPGS lesion counts are correlated, and grouped by size, according to Figure8.Note.Abbreviations as in Figure8.1 One was FN using FLAIR3Da; the rest were TP in FLAIR3Da.2Two were FN using FLAIR3Da; the rest were TP in FLAIR3Da.3Three were FN using FLAIR3Da; the rest were TP in FLAIR3Da.4Four were FN using FLAIR3Da; the rest were TP in FLAIR3Da.Lesion counts without superscripts were all TP in FLAIR3Da.

Figure 10 .
Figure 10.An MS patient received an MRI scan including three T2w FLAIR sequences: FLAIRUF (a), FLAIRTSE (b), and FLAIR3D (c).Five lesions can be seen in each picture, situated in the frontoparietal region.

Figure 10 .
Figure 10.An MS patient received an MRI scan including three T2w FLAIR sequences: FLAIR UF (a), FLAIR TSE (b), and FLAIR 3D (c).Five lesions can be seen in each picture, situated in the frontoparietal region.

Diagnostics 2024 , 35 Figure 12 .
Figure 12.Conspicuity ratings of lesions in the FLAIRUF images, categorized by size and location.Lesion conspicuity was significantly superior for large lesions compared to small lesions.Unless for small lesions, there was a significant difference among some brain regions for large lesions.This could be attributed to occipital lesions (superior) and central lesions (inferior).Note.Black = 1; dark gray = 2; gray = 3; light gray = 4; white = 5.Abbreviations and brain regions as in Table11.* p < 0.05.

Figure 12 .
Figure 12.Conspicuity ratings of lesions in the FLAIR UF images, categorized by size and location.Lesion conspicuity was significantly superior for large lesions compared to small lesions.Unless for small lesions, there was a significant difference among some brain regions for large lesions.This could be attributed to occipital lesions (superior) and central lesions (inferior).Note.Black = 1; dark gray = 2; gray = 3; light gray = 4; white = 5.Abbreviations and brain regions as in Table11.* p < 0.05.

Figure 13 .
Figure 13.SNR and CNR in FLAIRUF images (top) and FLAIR3Da images (bottom): (a) Inflammatory lesions.Continuous arrow: Large lesion in the right mesencephalon (approx.8 mm × 6 mm).The SNR appeared reduced in the center of the FLAIRUF image, thus decreasing the lesion conspicuity.Dotted arrows: Temporal lesions (right: approx.5 mm × 4 mm; left: approx.4 mm × 3 mm).The SNR appeared significantly improved in posterior brain regions in the FLAIRUF images, thus equaling lesion conspicuity between the image variants.Note that in FLAIR3Da, the left lesion was better visible in the adjacent image slice (not depicted).Arrowhead: Partially imaged, right temporal lesion (approx.3 mm × 1 mm in the slice image depicted).Comparison of adjacent slice images showed equal lesion conspicuity.(b) Arrow: Left frontal lesion (approx. 2 mm × 1 mm).Excellent lesion conspicuity in the FLAIRUF image due to very good SNR and CNR.(c) Infratentorial lesions.Continuous arrow: Large lesion (approx.9 mm × 6 mm) that was not visible in the FLAIRUF image owing to low SNR and CNR.Dotted arrow: Large lesion (approx.8 mm × 6 mm) that was less visible in the FLAIRUF image due to reduced SNR and CNR.Arrowhead: Large lesion (approx.3 mm × 2 mm) that was better visible in the FLAIRUF image.Note that the SNR improves toward the outer regions of the FLAIRUF image.

Figure 13 .
Figure 13.SNR and CNR in FLAIR UF images (top) and FLAIR 3Da images (bottom): (a) Inflammatory lesions.Continuous arrow: Large lesion in the right mesencephalon (approx.8 mm × 6 mm).The SNR appeared reduced in the center of the FLAIR UF image, thus decreasing the lesion conspicuity.Dotted arrows: Temporal lesions (right: approx.5 mm × 4 mm; left: approx.4 mm × 3 mm).The SNR appeared significantly improved in posterior brain regions in the FLAIR UF images, thus equaling lesion conspicuity between the image variants.Note that in FLAIR 3Da , the left lesion was better visible in the adjacent image slice (not depicted).Arrowhead: Partially imaged, right temporal lesion (approx.3 mm × 1 mm in the slice image depicted).Comparison of adjacent slice images showed equal lesion conspicuity.(b) Arrow: Left frontal lesion (approx. 2 mm × 1 mm).Excellent lesion conspicuity in the FLAIR UF image due to very good SNR and CNR.(c) Infratentorial lesions.Continuous arrow: Large lesion (approx.9 mm × 6 mm) that was not visible in the FLAIR UF image owing to low SNR and CNR.Dotted arrow: Large lesion (approx.8 mm × 6 mm) that was less visible in the FLAIR UF image due to reduced SNR and CNR.Arrowhead: Large lesion (approx.3 mm × 2 mm) that was better visible in the FLAIR UF image.Note that the SNR improves toward the outer regions of the FLAIR UF image.

35 Figure 14 .
Figure 14.Spatial distortion artifacts in the FLAIRUF images: (a) Frontal distortion artifact limiting the diagnostic information in that region.(b) Frontobasal distortion artifacts resulting in limited diagnostic information from that region.(c) Temporopolar distortion artifacts limiting the diagnostic information in the vicinity.In contrast, the cerebellar and pontine distortion artifacts do not limit diagnostic information.

Figure 14 .
Figure 14.Spatial distortion artifacts in the FLAIR UF images: (a) Frontal distortion artifact limiting the diagnostic information in that region.(b) Frontobasal distortion artifacts resulting in limited diagnostic information from that region.(c) Temporopolar distortion artifacts limiting the diagnostic information in the vicinity.In contrast, the cerebellar and pontine distortion artifacts do not limit diagnostic information.

Figure 14 .
Figure 14.Spatial distortion artifacts in the FLAIRUF images: (a) Frontal distortion artifact limiting the diagnostic information in that region.(b) Frontobasal distortion artifacts resulting in limited diagnostic information from that region.(c) Temporopolar distortion artifacts limiting the diagnostic information in the vicinity.In contrast, the cerebellar and pontine distortion artifacts do not limit diagnostic information.

Figure 15 .
Figure 15.Infratentorial pulsatile flow artifacts that severely limit diagnostic information in the FLAIRUF images and the FLAIR3Da images: (a) FLAIRUF (left) and FLAIR3Da as a reference (right), cerebellum: The continuous arrows depict an inflammatory true-positive lesion (approx.4 mm × 3 mm).The dotted arrow indicates a pulsation artifact that is prone to being confused with a lesion.It was counted as a small, false-positive lesion (approx. 2 mm × 1 mm).The arrowhead points to a pulsation artifact that is not likely to be confused with a lesion due to its typical location adjacent to the occipital sinus.(b) FLAIR3Da (left) and FLAIRUF as a reference (right), at the level of the pons: Top images: Typical hyperintense artifact band in the FLAIR3Da image; the arrowhead points to an intensely hyperintense spot within the artifact region that is part of the grainy texture of the artifact.Possible lesions within the artifact region would have been masked completely.Middle images: the continuous arrows show a large lesion (approx.8 mm × 3 mm) that was misinterpreted as part of the pulsation artifact in the FLAIR3Da image.Bottom images: The dotted arrow denotes a small, falsepositive FLAIR3Da lesion (approx. 2 mm × 2 mm) that turned out to be part of the pulsation artifact.Note.Corresponding slices could not be positioned exactly identically owing to different slice thicknesses including slice gaps and non-parallel slice inclinations.

Figure 15 .
Figure15.Infratentorial pulsatile flow artifacts that severely limit diagnostic information in the FLAIR UF images and the FLAIR 3Da images: (a) FLAIR UF (left) and FLAIR 3Da as a reference (right), cerebellum: The continuous arrows depict an inflammatory true-positive lesion (approx.4 mm × 3 mm).The dotted arrow indicates a pulsation artifact that is prone to being confused with a lesion.It was counted as a small, false-positive lesion (approx. 2 mm × 1 mm).The arrowhead points to a pulsation artifact that is not likely to be confused with a lesion due to its typical location adjacent to the occipital sinus.(b) FLAIR 3Da (left) and FLAIR UF as a reference (right), at the level of the pons: Top images: Typical hyperintense artifact band in the FLAIR 3Da image; the arrowhead points to an intensely hyperintense spot within the artifact region that is part of the grainy texture of the artifact.Possible lesions within the artifact region would have been masked completely.Middle images: the continuous arrows show a large lesion (approx.8 mm × 3 mm) that was misinterpreted as part of the pulsation artifact in the FLAIR 3Da image.Bottom images: The dotted arrow denotes a small, false-positive FLAIR 3Da lesion (approx. 2 mm × 2 mm) that turned out to be part of the pulsation artifact.Note.Corresponding slices could not be positioned exactly identically owing to different slice thicknesses including slice gaps and non-parallel slice inclinations.Diagnostics 2024, 14, x FOR PEER REVIEW 22 of 35

Figure 16 .
Figure 16.Minor artifacts in the FLAIRUF images: (a) Supratentorial pulsation artifacts, caused by extracerebral blood vessels: hyperintense (left image) or hypointense (right image).Hyperintense artifacts can usually be distinguished easily from a lesion, owing to its well-defined, sharply demarcated margin in relation to its size.Hyperintense and hypointense artifacts can usually be related to distant blood vessels, shifted along the phase encoding direction at fixed intervals corresponding to k-space sampling patterns (a quarter of the field of view for the protocol used in our study).Hyperintense artifacts are often located directly adjacent to hypointense artifacts in neighboring image slices.(b) Rare chemical shift artifacts due to incomplete fat suppression, in the shape of hyperintense (left image) or hypointense (right image) frontal streaks.(c) Rare residual aliasing, in the shape of a subtle hyperintense right central streak.(d) Rare spike artifacts, appearing in a herringbone pattern.(e) Supratentorial pulsation artifacts, caused by parenchymal blood vessels (singular findings): hyperintense (small FP lesion), insular cortex (left image), and hypointense, anterior limb of internal capsule (right image).They could not be distinguished using the FLAIR3D sequence, however, were clearly correlated with contrast-enhanced images.Also, note the ventricular cerebrospinal fluid pulsation artifact in the right image.

Figure 16 .
Figure 16.Minor artifacts in the FLAIR UF images: (a) Supratentorial pulsation artifacts, caused by extracerebral blood vessels: hyperintense (left image) or hypointense (right image).Hyperintense artifacts can usually be distinguished easily from a lesion, owing to its well-defined, sharply demarcated margin in relation to its size.Hyperintense and hypointense artifacts can usually be related to distant blood vessels, shifted along the phase encoding direction at fixed intervals corresponding to k-space sampling patterns (a quarter of the field of view for the protocol used in our study).Hyperintense artifacts are often located directly adjacent to hypointense artifacts in neighboring image slices.(b) Rare chemical shift artifacts due to incomplete fat suppression, in the shape of hyperintense (left image) or hypointense (right image) frontal streaks.(c) Rare residual aliasing, in the shape of a subtle hyperintense right central streak.(d) Rare spike artifacts, appearing in a herringbone pattern.(e) Supratentorial pulsation artifacts, caused by parenchymal blood vessels (singular findings): hyperintense (small FP lesion), insular cortex (left image), and hypointense, anterior limb of internal capsule (right image).They could not be distinguished using the FLAIR 3D sequence, however, were clearly correlated with contrast-enhanced images.Also, note the ventricular cerebrospinal fluid pulsation artifact in the right image.

Figure 17 .
Figure 17.Infratentorial pulsatile flow artifacts in FLAIRTSE (left), contrasted with correspond FLAIRUF slices (right).The artifact regions are marked with dotted arrows.They appear as irregu streaky bands traversing the cerebellum from the right sigmoid sinus to the left sigmoid sinus: (a) Artifact-induced hyper-and hypointense dots across the cerebellum; the FLAIRUF image c firms that there is no actual lesion in this area.(b) Artifact-induced hyper-and hypointense stre across the cerebellum; the FLAIRUF image confirms that there is one actual lesion (continuous arro in this area.Note.Corresponding slices could not be positioned exactly identically owing to diff ent slice intervals and non-parallel slice inclinations.

Figure 17 .
Figure 17.Infratentorial pulsatile flow artifacts in FLAIR TSE (left), contrasted with corresponding FLAIR UF slices (right).The artifact regions are marked with dotted arrows.They appear as irregular, streaky bands traversing the cerebellum from the right sigmoid sinus to the left sigmoid sinus: (a) Artifact-induced hyper-and hypointense dots across the cerebellum; the FLAIR UF image confirms that there is no actual lesion in this area.(b) Artifact-induced hyper-and hypointense streaks across the cerebellum; the FLAIR UF image confirms that there is one actual lesion (continuous arrow) in this area.Note.Corresponding slices could not be positioned exactly identically owing to different slice intervals and non-parallel slice inclinations.

Figure 18 .
Figure 18.Positional dependence of SNR and CNR within the FLAIRUF images: (a) Image slice at the level of the basal nuclei.The SNR deteriorates toward the center of the image, whereas the CNR remains relatively good.Note that both the SNR and CNR are excellent in the marginal regions of the cerebral cortex, e.g., in the occipital lobe.(b) Image slice at the level of the pons and the cerebellum.The SNR is substandard around the pons and improves exceedingly toward the posterior lobe of the cerebellum to an excellent level.The CNR, however, seems generally slightly substandard in this area compared to other brain regions, even in the most posterior regions.This may be associated with the characteristic, fine folium-sulcus texture of the cerebellum (e.g., vermis or posterior lobe), which cannot be distinguished clearly.

Figure 18 .
Figure 18.Positional dependence of SNR and CNR within the FLAIR UF images: (a) Image slice at the level of the basal nuclei.The SNR deteriorates toward the center of the image, whereas the CNR remains relatively good.Note that both the SNR and CNR are excellent in the marginal regions of the cerebral cortex, e.g., in the occipital lobe.(b) Image slice at the level of the pons and the cerebellum.The SNR is substandard around the pons and improves exceedingly toward the posterior lobe of the cerebellum to an excellent level.The CNR, however, seems generally slightly substandard in this area compared to other brain regions, even in the most posterior regions.This may be associated with the characteristic, fine folium-sulcus texture of the cerebellum (e.g., vermis or posterior lobe), which cannot be distinguished clearly.

Table 3 .
Lesion detection in FLAIR UF images and FLAIR 3Da images.

Table 3 .
Lesion detection in FLAIRUF images and FLAIR3Da images.

Table 4 .
Presumed causes of FN large lesions (≥3 mm total) in FLAIR UF and FLAIR 3Da images.

Table 5 .
Presumed causes of FN small lesions (<3 mm) in FLAIR UF and FLAIR 3Da images.

Table 6 .
Presumed causes of FP lesions in FLAIR UF and FLAIR 3Da images.

Table 6 .
Presumed causes of FP lesions in FLAIRUF and FLAIR3Da images.

Table 7 .
Lesion detectability in FLAIR TSE images and FLAIR UF counterparts.
Note.TSE = FLAIR TSE ; further abbreviations as in Table3and Figure8.To compare sensitivity results, McNemar's test was performed, based on paired data shown in Figure9.Unlike in the previous Section, the corresponding FP values (see Table10) were insufficient to ensure reliable PPV analysis results; hence, it was omitted here.

Table 8 .
Presumed causes of FN large lesions (≥3 mm total) in FLAIR TSE and corresponding FLAIR TSE images.

Table 8 .
Presumed causes of FN large lesions (≥3 mm total) in FLAIRTSE and corresponding FLAIRTSE images.

Table 9 .
Presumed causes of FN small lesions (<3 mm) in FLAIRTSE and corresponding FLAIRTSE images.

Table 9 .
Presumed causes of FN small lesions (<3 mm) in FLAIR TSE and corresponding FLAIR TSE images.

Table 10 .
Presumed causes of FP lesions in FLAIR UF and FLAIR TSE images.

Table 11 .
Conspicuity ratings of lesions in the FLAIR UF images, categorized by size and location.

Table 12 .
Average lesion diameters (lengthwise), grouped by size category and location.

Table 13 .
Conspicuity ratings of large lesions (≥3 mm), grouped by width category and location.
Note.The counts of lesions are given.Abbreviations and brain regions as in Table11.3.3.Image Quality3.3.1.FLAIR UF Compared with FLAIR 3Da

Table 14 .
SNR and CNR in FLAIR UF and FLAIR 3Da images.

Table 15 .
Artifacts in the FLAIR UF and FLAIR 3Da images.

Table 16 .
SNR and CNR in FLAIR UF and FLAIR TSE images.

Table 17 .
Artifacts in the FLAIR UF and FLAIR TSE images.

Table 18 .
FLAIR UF : SNR assessment in the vicinity of TP GS lesions, categorized by location.

Table 18 .
FLAIRUF: SNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 19 .
FLAIRUF: CNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 18 .
FLAIRUF: SNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 19 .
FLAIRUF: CNR assessment in the vicinity of TPGS lesions, categorized by location.Note.CNR = contrast-to-noise ratio; further abbreviations as in Table18.Brain regions as in Table11.The p-values were calculated using the chi-squared test.

Table 18 .
FLAIRUF: SNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 19 .
FLAIRUF: CNR assessment in the vicinity of TPGS lesions, categorized by location.Note.CNR = contrast-to-noise ratio; further abbreviations as in Table18.Brain regions as in Table11.The p-values were calculated using the chi-squared test.

Table 18 .
FLAIRUF: SNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 19 .
FLAIRUF: CNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 18 .
Brain regions as in Table11.The p-values were calculated using the chi-squared test.

Table 19 .
FLAIR UF : CNR assessment in the vicinity of TP GS lesions, categorized by location.

Table 18 .
FLAIRUF: SNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 19 .
FLAIRUF: CNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 18 .
FLAIRUF: SNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 19 .
FLAIRUF: CNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 18 .
Brain regions as in Table11.The p-values were calculated using the chi-squared test.

Table 18 .
FLAIRUF: SNR assessment in the vicinity of TPGS lesions, categorized by location.

Table 19 .
FLAIRUF: CNR assessment in the vicinity of TPGS lesions, categorized by location.Note.CNR = contrast-to-noise ratio; further abbreviations as in Table18.Brain regions as in Table11.The p-values were calculated using the chi-squared test.