Cedars-Sinai Medical Center
Department of Neurology

Author Of 5 Presentations

Imaging Oral Presentation

FC03.02 - A step forward toward the fully automated assessment of the central vein sign

Speakers
Presentation Number
FC03.02
Presentation Topic
Imaging
Lecture Time
13:12 - 13:24

Abstract

Background

A deep-learning prototype method, called CVSNet, was recently introduced for the automated detection of the central vein sign (CVS) in brain lesions and demonstrated effective and accurate discrimination of multiple sclerosis (MS) from its mimics. However, this method solely considered focal lesions displaying the central vein sign (CVS+) or not (CVS), therefore requiring a manual pre-selection of the lesions to be evaluated by eliminating the so-called excluded lesions (CVSe) as defined by the NAIMS criteria. CVSe lesions may however play an important role in differential diagnosis. Moreover, extending the automated CVS classification to these lesions would facilitate the integration of CVSNet with existing MS lesion segmentation algorithms in a fully automated pipeline.

Objectives

To develop an improved version of the CVSNet prototype method able to classify all types of lesions (CVS+, CVS and CVSe).

Methods

Patients with an established MS or CIS diagnosis (RRMS 29; SPMS 10; PPMS 10; CIS 1; mean ± SD age: 50 ± 11 years; male/female: 23/27), and healthy controls (n=8; mean ± SD age: 41 ± 9 years; male/female: 5/3), underwent 3T brain MRI (MAGNETOM Skyra and MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany, or Achieva, Philips Healthcare, Best, Netherlands). Brain lesions were automatically segmented and manually corrected by a single rater. CVS assessment was conducted on FLAIR* images by two raters, according to the NAIMS guidelines, yielding 1542 CVS+, 1004 CVS−, and 1131 CVSe lesions. A convolutional neural network (CNN) based on the CVSnet architecture was trained with different configurations using 3021 samples (1261 CVS+, 847 CVS, and 913 CVSe) and evaluated in 656 unseen samples (281 CVS+, 157 CVS−, and 218 CVSe, from 13 patients) for final testing. The configurations relied on different combinations of the following channels as input: (i) FLAIR*, (ii) T2*, (iii) lesion mask, and (iv) CSF and brain tissue concentration maps obtained from a partial-volume estimation algorithm. Lesion-wise classification performance was evaluated for the different configurations by estimating the sensitivity, specificity, and accuracy for each lesion class.

Results

The results were similar across the different configurations. The best performance in the unseen testing set was obtained when all channels were used as input (sensitivity: 0.71, 0.73; specificity: 0.71, 0.81; and accuracy: 0.71, 0.79 for CVS+, CVS−, respectively). For CVSe, this approach achieved 0.52 sensitivity, 0.94 specificity, and 0.80 accuracy.

Conclusions

We introduced a modified CVSNet prototype method that can analyze the presence of the central vein for all types of brain lesions, enabling its integration with current MS lesion segmentation algorithms. This new feature will allow a fully automated assessment of the CVS in patients’ brains, speeding up the evaluation of CVS as a diagnostic biomarker for differentiating MS from mimicking diseases.

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Invited Presentations Invited Abstracts

PS07.02 - 7-tesla high-resolution imaging of the MS brain

Speakers
Authors
Presentation Number
PS07.02
Presentation Topic
Invited Presentations
Lecture Time
13:00 - 13:15
Imaging Oral Presentation

PS11.04 - Quantitative susceptibility mapping classifies white matter lesions with different myelin and axonal content and quantifies diffuse pathology in MS

Abstract

Background

Quantitative susceptibility mapping (QSM) identifies iron accumulation and myelin loss in smoldering white matter lesions (WMLs). Yet, QSM may be also used to provide a broader understanding of focal and diffuse MS pathology.

Objectives

To study QSM features across WMLs, to assess myelin and axonal loss in WMLs with different QSM features and to quantify QSM pathology in normal-appearing white and cortical grey matter (NAWM, NAGM).

Methods

Ninety-one MS patients (62 RRMS, 29 PMS) and 72 healthy controls (HC) underwent QSM, myelin water imaging (MWI) and multishell diffusion at 3T MRI. In WMLs, cortical lesions (CLs), NAWM and NAGM, we extracted mean QSM, myelin water fraction (MWF) and neurite density index (NDI). WMLs were classified into 5 groups according to their appearance on 3D-EPI QSM: (i) isointense; (ii) with hyperintense rim, Rim+ (iii); with hypointense rim relative to the lesion core, hypo Rim; (iv) hyperintense; (v) hypointense. Mann-Whitney and Kruskal-Wallis test with Dunn’s correction for multiple comparison were used to compare (a) lesion types and (b) specific lesions vs all other WMLs. Voxel-wise comparisons of NAWM QSM were performed using Threshold-Free Cluster Enhancement (TFCE) clustering. Cortical analysis of QSM NAGM and GM-HC was performed using FreeSurfer and compared using a General Linear model (GLM).

Results

Of 1136 WMLs in QSM maps, we detected: (i) 314 (27.6%), (ii) 183 (16.1%), (iii) 16 (1.41%), (iv) 577 (50.8%) and (v) 46 (4.05%) WML. All WML exhibited lower NDI than NAWM and WM-HC (P<0.0001). Isointense lesions exhibited higher NDI (P=0.0115) and MWF (P<0.0001) than other WMLs. Rim + and hyperintense lesions exhibited lower MWF than NAWM and WM-HC (P<0.0001). Rim + lesions showed lower MWF and NDI than other WML types (P<0.001). Hypo Rim+ lesions and hypointense lesions exhibited higher MWF than other WMLs (P=0.0006, P<0.05). Hyperintense lesions exhibited lower MWF than other WMLs types (P<0.01) except Rim+ lesions. TFCE and vertex-wise cortical surface analysis showed areas throughout the NA tissue, where QSM is either lower or higher compared to healthy tissue in HC and in PMS compared to RMS (P<0.01).

Conclusions

QSM is sensitive to diffuse and focal pathology with various myelin and axonal characteristics. We hypothesize that isointense WMLs show high repair activity, hypointense WMLs are remyelinated lesions and hyperintense WMLs are chronic inactive lesions. MRI-histopathology work is ongoing to confirm these findings.

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Imaging Oral Presentation

PS11.05 - Inclusion of small ovoid lesions in central vein sign assessment improves sensitivity for multiple sclerosis

Speakers
Presentation Number
PS11.05
Presentation Topic
Imaging
Lecture Time
10:09 - 10:21

Abstract

Background

The ‘central vein sign’ (CVS) is increasingly recognized as a valuable biomarker with high specificity and sensitivity for multiple sclerosis (MS) MRI lesions. Current consensus North American Imaging in Multiple Sclerosis (NAIMS) guidelines recommend excluding lesions <3mm in diameter in any plane for CVS assessment. However, different lesion-size exclusion cut-offs for CVS have not been systematically evaluated.

Objectives

To evaluate the impact of different lesion size cut-offs and exclusion methodologies on CVS analysis and select3* criteria for MS diagnosis.

Methods

MS patients and non-MS controls were recruited as part of the National Institute of Neurological Disorders and Stroke MS natural history study and underwent 3T MRI on either Siemens Skyra or Philips Achieva scanners. MS lesions were segmented using a deep learning-based method and manually corrected by a single rater. Individual lesions were extracted as clusters of connected voxels, and their principal axes lengths (calculated as the lengths of the major axes of an ellipsoid with the same normalized second central moments) were used to measure lesion size in 3 dimensions. Ground truth CVS assessment was conducted by two raters on all lesions regardless of size. Two paradigms of lesion exclusion were compared: (1) excluding lesions if any dimension was less than threshold (ExcAny), or (2) if all dimensions were less than threshold (ExcAll).

Results

A total of 3920 lesions from 71 subjects (8 healthy controls, 36 RRMS, 12 SPMS, 14 PPMS, 1 CIS) were included in the analysis. CVS+ lesions were more likely to be ovoid and less spherical compared to their CVS- counterparts, as measured by the fractional anisotropy of lesion dimensions (mean difference 0.02, p=0.001). Of the 1679 CVS+ lesions in the cohort, 82% met the ExcAny criteria to be excluded at a 3mm cut-off, which was reduced to 29% when ExcAll criteria were used (McNemar test, p < 0.001). At the subject-level, an increase in the sensitivity of select3* CVS criteria for MS diagnosis was noted at 3mm using the less strict ExcAll (95%) compared to the more conservative ExcAny criteria (61%), without impacting specificity (100% for both methods). There was a reduction in specificity for both ExcAny and ExcAll criteria when size cut-offs less than or equal to 2mm were used (88% for both).

Conclusions

Compared to the current NAIMS guidelines, ExcAll criteria for CVS lesion analysis allow the inclusion of a larger proportion of CVS+ lesions and improve the sensitivity of select3* criteria for MS diagnosis. These findings improve the applicability of the CVS as a diagnostic marker for MS in clinical practice and provide evidence for future modifications of CVS lesion exclusion guidelines.

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Machine Learning/Network Science Oral Presentation

PS16.04 - RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesions assessment in multiple sclerosis

Speakers
Presentation Number
PS16.04
Presentation Topic
Machine Learning/Network Science
Lecture Time
13:27 - 13:39

Abstract

Background

In multiple sclerosis (MS), perilesional chronic inflammation appears on in vivo 3T susceptibility-based magnetic resonance imaging (MRI) as non-gadolinium-enhancing paramagnetic rim lesions (PRL). A higher PRL burden has been recently associated with a more aggressive disease course. The visual detection of PRL by experts is time-consuming and can be subjective.

Objectives

To develop a multimodal convolutional neural network (CNN) capable of automatically detecting PRL on 3D-T2*w-EPI unwrapped phase and 3D-T2w-FLAIR images.

Methods

124 MS cases (87 relapsing remitting MS, 16 primary progressive MS and 21 secondary progressive MS) underwent 3T MRI (MAGNETOM Prisma and MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Two neurologists visually inspected FLAIR magnitude and EPI phase images and annotated 462 PRL. 4857 lesions detected by an automatic segmentation (La Rosa et al. 2019) without overlap with PRL were considered non-PRL. The prototype RimNet was built upon two single CNNs, each fed with 3D patches centered on candidate lesions in phase and FLAIR images, respectively. A two-step feature-map fusion, initially after the first convolutional block and then before the fully connected layers, enhances the extraction of low and high-level multimodal features. For comparison, two unimodal CNNs were trained with phase and FLAIR images. The areas under the ROC curve (AUC) were used for evaluation (DeLong et al. 1988). The operating point was set at a lesion-wise specificity of 0.95. The patient-wise assessment was conducted by using a clinically relevant threshold of four rim+ lesions per patient (Absinta et al. 2019).

Results

RimNet (AUC=0.943) outperformed the phase and FLAIR image unimodal networks (AUC=0.913 and 0.855, respectively, P’s <0.0001). At the operating point, RimNet showed higher lesion-wise sensitivity (70.6%) than the unimodal phase network (62.1%), but lower than the experts (77.7%). At the patient level, RimNet performed with sensitivity of 86.8% and specificity of 90.7%. Individual expert ratings yielded averaged sensitivity and specificity values of 76.3% and 99.4%, respectively.

Conclusions

The excellent performance of RimNet supports its further development as an assessment tool to automatically detect PRL in MS. Interestingly, the unimodal FLAIR network performed reasonably well despite the absence of a paramagnetic rim, suggesting that morphometric features such as volume or shape might be a distinguishable feature of PRL.

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Presenter Of 1 Presentation

Invited Presentations Invited Abstracts

PS07.02 - 7-tesla high-resolution imaging of the MS brain

Speakers
Authors
Presentation Number
PS07.02
Presentation Topic
Invited Presentations
Lecture Time
13:00 - 13:15

Invited Speaker Of 1 Presentation

Invited Presentations Invited Abstracts

PS07.02 - 7-tesla high-resolution imaging of the MS brain

Speakers
Authors
Presentation Number
PS07.02
Presentation Topic
Invited Presentations
Lecture Time
13:00 - 13:15

Author Of 9 Presentations

Machine Learning/Network Science Poster Presentation

P0016 - Progressive multifocal leukoencephalopathy lesion and brain parenchymal segmentation from MRI using serial deep convolutional neural networks (ID 1531)

Speakers
Presentation Number
P0016
Presentation Topic
Machine Learning/Network Science

Abstract

Background

Progressive multifocal leukoencephalopathy (PML) is a rare opportunistic brain infection caused by the JC virus associated with significant morbidity and mortality, which can occur in the context of certain MS disease modifying therapies. There are currently no validated automatic methods for quantification of PML lesion burden or brain atrophy on MRI.

Objectives

We assessed whether deep learning techniques can be employed for automated brain parenchymal and lesion segmentation in PML using an approach dubbed “JCnet,” named after the causative viral agent.

Methods

We performed a retrospective analysis of PML patients who were evaluated at the NIH Neuroimmunology Clinic. MRI scans were acquired on either a Siemens Skyra or a Philips 3T MRI scanners. For PML brain and lesion segmentation, we implement a 3D patch-based approach with two consecutive fully convolutional neural networks (CNNs) with a feature pyramid architecture. The first network performs brain extraction as foreground, with meninges and cerebrospinal fluid spaces as background , while the second segments the underlying PML lesion(s). We measured the segmentation accuracy using Dice similarity coefficient (DSC) and absolute volume differences (AVD). We evaluated JCnet against methods designed for normal-appearing brain segmentation, FSL/FMRIB's Automated Segmentation Tool (FAST) and FreeSurfer, as well as MS lesion segmentation, Lesion Segmentation Toolbox (LST) and Lesion-TOpology-preserving Anatomical Segmentation (LTOADS). Comparisons were performed using Wilcoxon matched-pairs signed-ranks test.

Results

A total of 41 PML patients (mean age 55 years, SD 13; 44% female) were included in the analysis. The cohort was empirically divided into 31 training and 10 testing cases sampled at random. The mean time between PML onset and MRI acquisition was 4.5 months (range 0.6 – 44.5 months). JCnet resulted in a 4% and 64cm3 absolute improvement in DSC and AVD compared to FAST (p=0.005 and 0.01), and a 6% and 41cm3 absolute improvement compared to FreeSurfer respectively (p=0.005 and p=0.02). This was driven in part by improved segmentation of brain tissue within T1-hypointense PML lesions. For PML lesion segmentation, there was an absolute improvement of 42% and 14cm3 in DSC and AVD respectively compared to LST, and 53% and 19cm3 absolute improvements compared to LTOADS respectively (p=0.005 for all lesion comparisons). This was driven by improved sensitivity of supra- and infratentorial PML lesion identification and segmentation.

Conclusions

We employ an end-to-end deep learning-based method for automated segmentation of lesion and brain parenchymal volume in PML. By tracking quantitative measurements of PML-related MRI changes, this approach provides a window for clinicians and scientists to accurately monitor PML radiographically and its response to experimental therapies.

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Biomarkers and Bioinformatics Poster Presentation

P0083 - Gadolinium improves detection of central vein lesions in MS using 3T FLAIR*. (ID 1404)

Abstract

Background

The central vein sign (CVS) is a proposed MRI diagnostic biomarker for multiple sclerosis (MS). Use of gadolinium (Gd) in the CVS literature has been inconsistent, and it is unknown whether Gd improves detection of CVS when using FLAIR*.

Objectives

To determine if, and to what extent, gadolinium injection improves detection of CVS lesions when using FLAIR* imaging.

Methods

A cross-sectional multicenter study recruited adults clinically and/or radiologically suspected of having MS. High-isotropic-resolution, T2*-weighted segmented echo-planar imaging (T2*-EPI) was acquired pre- and post-injection of Gd-based contrast agent at 3T; pre-Gd 3D FLAIR images were also acquired. T2*-EPI and FLAIR images were processed on the QMENTA platform to generate FLAIR* images. FLAIR* pre-Gd and post-Gd scans from this substudy of 30 patients at 5 sites were analyzed. FLAIR images were used to create T2 lesion masks. Subsequently, FLAIR* images were evaluated in a random order. Lesions were categorized as CVS+, CVS-, or excluded based on the North American Imaging in MS (NAIMS) Criteria by two trained raters blinded to clinical data and Gd use. The proportion of CVS+ lesions was calculated for each scan, and differences in CVS detection based on Gd use were assessed by a Wilcoxon rank-sum test. Diagnostic performance was compared against McDonald 2017 Criteria.

Results

The mean participant age was 45 years (SD: 12); 23 (77%) were women. 14 (47%) met McDonald 2017 Criteria for MS, while 16 (53%) did not (“non-MS”). A total of 487 CVS+ lesions and 976 CVS- lesions were evaluated. The percentage of CVS+ lesions post-Gd in the MS group (median 67% [IQR 30%]) was higher than pre-Gd (41% [47%], p<0.001). There was no apparent difference in percentage of CVS+ lesion in the non-MS group (post-Gd: 10% [23%]; pre-Gd: 5% [29%]; p=0.1). In the MS group, 12/14 (86%) had ≥40% CVS+ lesions on post-Gd imaging, whereas only 8/14 (57%) exceeded that threshold on pre-Gd imaging. When evaluating CVS performance using the 40% CVS+ threshold, the sensitivity and specificity of the CVS post-Gd for MS were 86% and 81%, respectively, compared to 54% and 86% pre-Gd.

Conclusions

The detection of the CVS using FLAIR* at 3T is improved when Gd is used. Based on these results, a multicenter prospective CVS diagnostic study, sponsored by NINDS and NAIMS, will use Gd in the study protocol. Future clinical use of the CVS should balance the increased costs and potential risks of Gd use with the risks of misdiagnosis due to missing CVS on non-contrast imaging.

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Biomarkers and Bioinformatics Poster Presentation

P0135 - Preventing multiple sclerosis misdiagnosis using the “central vein sign”: A real-world study (ID 500)

Speakers
Presentation Number
P0135
Presentation Topic
Biomarkers and Bioinformatics

Abstract

Background

Misdiagnosis of multiple sclerosis (MS) is common and often occurs due to misattribution of non-MS magnetic resonance imaging (MRI) lesions to MS demyelination. A recently developed MRI biomarker, the central vein sign (CVS), has demonstrated high specificity for MS lesions and may thus help prevent misdiagnosis.

Objectives

We explored the potential “real world” value of CVS in preventing MS misdiagnosis by comparing CVS in patients with MS and patients previously misdiagnosed with MS by standard clinical practice and established diagnostic tools.

Methods

30 patients (15 with MS and 15 misdiagnosed with MS) were prospectively recruited to undergo 3T brain MRI. T2-weighted fluid-attenuated inversion recovery (FLAIR) and T2*-weighted segmented echo-planar-imaging (T2*-EPI) were acquired to generate FLAIR* images, then analyzed by two independent raters blinded to clinical information. The percentage of lesions with CVS was calculated for each patient.

Results

The number of brain lesions per patient did not significantly differ between the two groups. The number of lesions with CVS, however, did differ, with a mean of 0.93 lesions (range 0-6) in the misdiagnosed group versus 6.3 (range 0-15) in the MS group. A CVS lesion threshold of 29% or higher resulted in high adjusted sensitivity of 0.79 (95% CI: 0.68-0.95) and specificity of 0.88 (95% CI: 0.68-0.95) for MS and correctly identified 87% of patients previously misdiagnosed with MS. Interrater reliability for CVS was excellent with a Cohen’s kappa coefficient of 0.86.

Conclusions

Our study found that CVS differentiated with high sensitivity and specificity patients with MS from patients who were previously misdiagnosed with MS after undergoing routine clinical evaluation. We believe that our findings further support the incorporation of CVS in the diagnostic approach to MS to increase accuracy and reduce misdiagnosis.

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Diagnostic Criteria and Differential Diagnosis Poster Presentation

P0261 - Paramagnetic rim lesions are specific to multiple sclerosis: an international multicenter 3T MRI study (ID 1025)

Abstract

Background

In multiple sclerosis (MS), a subset of chronic active white matter lesions are identifiable on MRI by their paramagnetic rims, and increasing evidence supports their association with clinical disease severity.

Objectives

To assess the prevalence and MS-specificity of paramagnetic rim lesions (PRL) on 3-tesla susceptibility-based MR brain images in MS vs non-MS cases in a multicenter sample drawn from 5 academic research hospitals at sites in Europe (Brussels, Lausanne, Milan) and the United States (NIH and JHU).

Methods

On submillimetric 3D T2*-segmented EPI brain MRI, the presence of PRL and central vein sign (CVS) were evaluated in the supratentorial brain of adults with MS (n=329) and non-MS neurological conditions (n=83). Non-MS cases were grouped as follows: (1) other-inflammatory neurological diseases (n=41); (2) HTLV-associated myelopathy/tropical spastic paraparesis (HAM/TSP; n=10); (3) HIV-infected (n=10); (4) non-inflammatory neurological diseases (n=22).

ROC curve analysis, with diagnosis as dependent variable (MS vs non-MS), was applied to examine the diagnostic accuracy for each biomarker (PRL and CVS). Youden’s index method was used to obtain the optimal cutoff value for each biomarker.

Results

PRL were detected in 172/329 (52%) of MS cases vs. 6/83 non-MS cases (7%).

In MS, 58% of progressive cases had at least one PRL, compared to 50% of relapsing cases. MS cases with more than 4 PRL were more likely to have higher disability scores (EDSS, MSSS and ARMSS), but not significantly longer disease duration or older age.

In non-MS cases, PRL were seen exclusively in only a few inflammatory/infectious neurological conditions, including Susac syndrome (3 cases), neuromyelitis optica spectrum disorder (1 case), Sjögren disease (1 case) and HAM/TSP (1 case). Unlike in MS, PRL in non-MS cases were not associated with a high frequency of CVS+ lesions.

The identification of at least one PRL (optimal cutoff) was associated with high diagnostic specificity (93%), but relatively low sensitivity (52%) and accuracy (area under ROC curve=0.77), whereas CVS detection alone (optimal cutoff 35.5-38%) could better discriminate MS from non-MS cases with high specificity (96%), sensitivity (99%), and accuracy (area under ROC curve=0.99). The combination of the two biomarkers further improved the specificity (99%), but sensitivity remained low (59%).

Conclusions

PRL yielded high specificity for MS lesions. Future prospective multicenter studies should further validate its role as a diagnostic biomarker.

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Imaging Poster Presentation

P0542 - Assessment of central vein sign conspicuity in multicenter 3T FLAIR* imaging (ID 985)

Abstract

Background

The central vein sign (CVS) is a proposed diagnostic biomarker for MS that can be identified using FLAIR*. The robustness of 3T FLAIR*, with and without the injection of gadolinium contrast agent (Gd), for imaging the CVS in a multicenter setting has not yet been demonstrated.

Objectives

To assess the conspicuity of the CVS on 3T FLAIR* imaging acquired across different sites with and without the injection of Gd.

Methods

A cross-sectional multicenter study recruited adults with a clinical and/or radiological suspicion of having MS from 10 sites within the North American Imaging in MS (NAIMS) Cooperative. High-isotropic-resolution T2*-weighted segmented echo-planar imaging (T2*-EPI) was acquired at 3T, pre- and post-injection of Gd, along with 3D FLAIR on different scanner brands and models. T2*-EPI and FLAIR images were processed on an online imaging platform (QMENTA) to generate FLAIR* images. To objectively assess the conspicuity of the CVS inside MS lesions, lesions and veins were segmented automatically and used to compute lesion-to-vein contrast-to-noise ratio (CNR) measures. ANOVA was used to compare CNR values across sites with post-hoc Tukey Honest Significant Difference testing. Multiple testing between sites was considered by controlling the false discovery rate. One-sided paired t-testing was used to compare the overall lesion-to-vein CNR values between pre- and post-Gd FLAIR*.

Results

Seventy-eight patients from nine sites were included in the analysis; one site was excluded due to low enrollment. The overall mean(coefficient of variation, CV) lesion-to-vein CNR values across the nine sites were 0.35(14%) and 0.37(12%) for pre- and post-Gd FLAIR*, respectively. Excluding an additional site that used an unharmonized FLAIR acquisition, the resulting mean(CV) CNR values were 0.36(12%) for pre-Gd and 0.37(11%) for post-Gd FLAIR*. Across most sites, there was a significant improvement in lesion-to-vein CNR measures for post-Gd compared to pre-Gd FLAIR* [mean difference = 0.011, p < 0.001, 95% CI: (0.008,0.015)].

Conclusions

Lesion-to-vein CNR measures across sites are in line with values first published for 3T FLAIR* and demonstrate the robustness of 3T FLAIR* for imaging the CVS in a multicenter setting. Moreover, there was an increase in vein conspicuity with improvement in CNR on post-Gd FLAIR*. Based on these results, a prospective multicenter NAIMS CVS diagnostic study, sponsored by NINDS, will use 3T FLAIR* imaging with Gd in the study protocol.

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Imaging Poster Presentation

P0562 - Cortical lesions are not associated with leptomeningeal enhancement in a cohort of adults with MS. (ID 420)

Speakers
Presentation Number
P0562
Presentation Topic
Imaging

Abstract

Background

Focal leptomeningeal enhancement (LME) on MRI is more commonly seen in neuroinflammatory diseases than in noninflammatory neurological diseases or healthy controls. In MS, meningeal inflammatory infiltrates sometimes overlie cortical demyelination in pathological samples, but studies linking cortical lesions to LME have had equivocal results to date.

Objectives

To evaluate the association between LME and cortical lesions in vivo and to assess the relationship between LME number and disease severity.

Methods

59 adults with MS (40 with relapsing remitting MS and 19 with primary or secondary progressive MS) underwent clinical testing (Multiple Sclerosis Functional Composite), 3 tesla (3T) MRI with gadolinium, and 7T non-gadolinium MRI within 6 months of the 3T MRI. 7T T1w MP2RAGE and T2*w gradient-echo images (both 0.5mm isometric) were used to identify cortical lesions, which were classified as leukocortical, intracortical, or subpial. Foci of LME were identified using post-gadolinium T2-FLAIR images and post–pre T2-FLAIR subtraction images. The spatial relationship between LME and cortical lesions was investigated, as was the clinical relationship between LME number and disease severity.

Results

66% of individuals (39/59) had no LME. 20% (12/59) had 1 focus of LME, and 14% (8/59) had >1 focus of LME. Median cortical lesion number was 20 in people without LME (IQR 91, range 0–206), 18 in people with 1 LME (IQR 12, range 0–182, p>0.05 vs no LME), and 39 in people with >1 LME (IQR 64, range 4–133, p>0.05 vs no LME). There was no difference in leukocortical, intracortical, or subpial lesion number between people with 0, 1, or >1 LME (p>0.05). None of the identified foci of LME was adjacent to a cortical lesion, though 13% of LME foci (4/31) were situated in the same sulcus as a cortical lesion. Median expanded disability status scale (EDSS) was higher in people with >1 focus of LME (5.5, range 1–7.5) compared to people without LME (median 1.5, range 0–7, p<0.05). EDSS was correlated with total cortical lesion number (rs=0.507, p<0.0001, ß=0.024) and subpial lesion number (rs=0.462, p<0.001, ß=0.028).

Conclusions

There was no association between number of LME foci and number of total cortical lesions or any cortical lesion subtype in our data. This suggests that LME cannot be taken to indicate ongoing inflammation overlying cortical demyelination. Further studies are needed to determine the histopathological basis of focal LME in MS and its relation, if any, to prior leptomeningeal inflammation.

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Imaging Poster Presentation

P0612 - New cortical lesions are rare in an MS cohort with stable white matter lesions: a 7T multicontrast longitudinal study (ID 1866)

Speakers
Presentation Number
P0612
Presentation Topic
Imaging

Abstract

Background

Cortical lesions are common and often extensive in MS, and have been associated with worse disability and progressive disease. There is limited evidence that cortical lesions continue to form in progressive phases of the disease, when new white matter lesion formation is minimal, perhaps offering an explanation for worsening disability in progressive MS.

Objectives

We longitudinally characterized cortical lesions in an MS cohort with stable white matter lesion burden in the year prior to enrollment to determine whether new cortical lesions are more frequent in people with worsening disability.

Methods

45 adults with MS (30 relapsing remitting (RR), 13 secondary progressive (SP), and 2 primary progressive (PP)), underwent 7T brain MRI (T2*w and MP2RAGE, each with 0.5mm isometric resolution), 3T brain and spine MRI, and clinical evaluation annually for 1 year. Cortical lesions were segmented manually on 7T images and categorized as leukocortical, intracortical, or subpial. White matter and spinal cord lesion burden were also determined.

Results

At baseline, 93% of individuals (42/45) had at least 1 cortical lesion. Median cortical lesion number was higher in progressive MS (median 55, interquartile range (IQR) 96, range 2–177) than RRMS (median 15, IQR 21, range 0–108; p<0.01). Cortical lesion volume correlated with physical and cognitive measures of disability. There was only a weak correlation between subpial and white matter lesion volume (r=0.35, p<0.05). During 1 year of follow-up, 6 people (4 RR, 2 SP) developed 1 new cortical lesion each. 4 of the 6 new cortical lesions were leukocortical, 1 was intracortical, and 1 was subpial. 5 people developed new white matter lesions, none of whom developed a new cortical lesion. In 2 people, we observed white matter lesions expand into the cortex. 3/6 people with new cortical lesions were on highly effective disease-modifying therapy during the follow up period. There was no difference in new cortical lesion or new white matter lesion number in people with stable vs worsening disability.

Conclusions

Using sensitive 7T MRI techniques, cortical lesions are detected in almost all MS cases. Cortical lesions are associated with worse and progressive disability and may form independently from white matter lesions. New cortical lesions appear to form infrequently in people with stable white matter lesions, however current disease-modifying therapies may not be completely effective at stopping cortical lesion formation.

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Imaging Poster Presentation

P0647 - Studying intralesional axonal damage in MS white matter lesions with diffusion MRI biophysical models (ID 694)

Abstract

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide new information on the microstructural properties of the tissue.

Objectives

To investigate the differences in intra-axonal signal fraction (IASF) between perilesional normal-appearing white matter (pl-NAWM), white matter lesions (WML) without (rim-) and with paramagnetic rim (rim+) comparing eight biophysical diffusion models.

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 yrs, median EDSS: 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution and b-values [0, 700, 1000, 2000, 3000] s/mm2.

Lesion masks were generated with a deep-learning-based method and manually corrected if required; pl-NAWM was defined as a region of 3-voxels around each WML; 225 paramagnetic rim lesions were manually identified based on 3D EPI and 2330 were labelled as rim-.

The following microstructural models were applied: Ball and Stick, Ball and Rockets, AMICO-NODDI, SMT-NODDI, MCMDI, NODDIDA, CHARMED, Microstructure Bayesian approach.

Delta (WML - pl-NAWM) was calculated for each WML, and one-side Mann Whitney U was used to compare the delta between models, followed by Bonferroni to correct for multiple testing.

Mean difference and Cohen's d was used to assess differences between lesions with extensive axonal damage (rim+) and other WML (rim-).

Results

All models applied in this study reported low IASF in rim+ WML, medium IASF in rim- WML and relatively high IASF in pl-NAWM. However, a broad spectrum of IASF values was identified from the different models: relatively simple models such as Ball and Stick and CHARMED, showed low delta IASF within lesions, while MCMDI models reported the highest significant difference compared to other models (p<0.0001). The comparison between WML and pl-NAWM mean IASF across models showed that MCDMI exhibited the highest difference (mean 0.13, Cohen’s d 1.34). AMICO-NODDI and SMT-NODDI showed close results (mean difference 0.12/0.12 and Cohen’s d 1.46/1.51).

The models best discriminating IASF between rim+ and rim- lesions were MCMDI and NODIDDA (mean 0.08/0.07, Cohen’s d -0.69/-0.70).

Conclusions

We compared eight WM diffusion models for assessment of intralesional axonal damage in MS patients. The comparison between WML and pl-NAWM showed that robustness of the method, identified with SMT-based and NODDI-based models, it is crucial. For the comparison between lesions with a high level of damage (rim +) and other WML, the diffusivity estimation appeared to play an important role. The method which appeared both robust and able to estimate the diffusivity of the tissue was MCMDI, which performed best in both cases.

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Invited Presentations Invited Abstracts

TC01.01 - Technical aspects of 7T imaging as it pertains to MS (at a level geared to neurologists) (ID 1993)

Speakers
Authors
Presentation Number
TC01.01
Presentation Topic
Invited Presentations

Abstract

Abstract

The main objective of this course is to familiarize MS clinicians with the technical basics of 7T MRI. I will first present the benefits of imaging the central nervous system at 7T and introduce some safety considerations for scanning a clinical population at this ultra-high field strength. I will then review examples of 7T brain images acquired on MS patients using state-of-the-art sequences and discuss some of the current technical limitations. Finally, I will mention promising technical developments aimed at overcoming these issues and facilitating the integration of 7T imaging in the clinical workflow.

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Presenter Of 1 Presentation

Invited Presentations Invited Abstracts

TC01.01 - Technical aspects of 7T imaging as it pertains to MS (at a level geared to neurologists) (ID 1993)

Speakers
Authors
Presentation Number
TC01.01
Presentation Topic
Invited Presentations

Abstract

Abstract

The main objective of this course is to familiarize MS clinicians with the technical basics of 7T MRI. I will first present the benefits of imaging the central nervous system at 7T and introduce some safety considerations for scanning a clinical population at this ultra-high field strength. I will then review examples of 7T brain images acquired on MS patients using state-of-the-art sequences and discuss some of the current technical limitations. Finally, I will mention promising technical developments aimed at overcoming these issues and facilitating the integration of 7T imaging in the clinical workflow.

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Invited Speaker Of 1 Presentation

Invited Presentations Invited Abstracts

TC01.01 - Technical aspects of 7T imaging as it pertains to MS (at a level geared to neurologists) (ID 1993)

Speakers
Authors
Presentation Number
TC01.01
Presentation Topic
Invited Presentations

Abstract

Abstract

The main objective of this course is to familiarize MS clinicians with the technical basics of 7T MRI. I will first present the benefits of imaging the central nervous system at 7T and introduce some safety considerations for scanning a clinical population at this ultra-high field strength. I will then review examples of 7T brain images acquired on MS patients using state-of-the-art sequences and discuss some of the current technical limitations. Finally, I will mention promising technical developments aimed at overcoming these issues and facilitating the integration of 7T imaging in the clinical workflow.

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