Spinal cord volume quantification and clinical application in multiple sclerosis

Magnetic resonance imaging of the spinal cord is a valuable part of the diagnostic work-up in patients with multiple sclerosis and other neurological disorders. Currently, mainly signal intensity changes within the cord in MR-images are considered in the clinical management of disorders of the central nervous system. However, cross-sectional or longitudinal measurements of spinal cord volume may deliver additional valuable information. Hence, the overall goal of this doctoral thesis was twofold: i) to clinically validate methods for quantification of spinal cord volume and spinal cord compartments, which are suitable for longitudinal assessment of large patient cohorts and clinical practice and ii) to evaluate spinal cord volume as a potential valuable biomarker and provide new insights into the role of spinal cord damage in multiple sclerosis. The first part focuses on the validation of quantification methods for spinal cord volume and includes two projects. While several MRI-based approaches of semi- and fully automatic techniques for volumetric spinal cord measurements have been proposed, up to now no gold standard exists and only a few methods have been validated and/or evaluated on patient follow-up scans to demonstrate their applicability in longitudinal settings. One of the latter segmentation methods was recently developed in-house and showed excellent reliability for cervical cord segmentation (Cordial, the cord image analyzer). In a first project, we extended its applicability to the lumbar cord, since no other software has been tested so far within this anatomical region of interest. On a well-selected dataset of 10 healthy controls (scanned in a scan-rescan fashion) we were able to show that - even within this technically challenging region - this segmentation algorithm provides excellent inter- and intra-session reproducibility showing high potential for application in longitudinal trials. In a second project, we aimed at obtaining volumetric information on particular compartments of the spinal cord such as the cord grey and white matter, since recent studies in multiple sclerosis provided evidence that measuring spinal cord grey matter volume changes may be a better biomarker for disease progression than quantifying cord white matter pathology or even volumetric brain measures. We therefore implemented a novel imaging approach, the averaged magnetization inversion recovery acquisitions sequence, for better grey and white matter visualization within the cord and scanned 24 healthy controls in a scan-rescan fashion. Further we applied an innovative fully automatic variational segmentation algorithm with a shape prior modified for 3D data with a slice similarity prior to segment the spinal cord grey and white matter. This pipeline allowed for highly accurate and reproducible grey and white matter segmentation within the cord. In view of its features, our automatic segmentation method seems promising for further application in both cross-sectional and longitudinal and in large multi-center studies. The second goal of this thesis was the clinical application of the above-mentioned methods for the evaluation of spinal cord volume changes as a potential biomarker in multiple sclerosis patients. For this purpose, we quantified spinal cord volume change in a large cohort of 243 multiple sclerosis patients, followed over a period of 6 years with annual clinical and MRI examinations. Spinal cord volume proved to be a strong predictor of physical disability and disease progression, indicating that it may be a suitable marker for monitoring disease activity and severity in all disease types but especially in progressive multiple sclerosis. Spinal cord volume also proved to be the only MRI metric to strongly explain the clinical progression over time as opposed to brain atrophy and lesion measures

Leptomeningeal enhancement in multiple sclerosis and other neurological diseases: A systematic review and Meta-Analysis

BACKGROUND The lack of systematic evidence on leptomeningeal enhancement (LME) on MRI in neurological diseases, including multiple sclerosis (MS), hampers its interpretation in clinical routine and research settings. PURPOSE To perform a systematic review and meta-analysis of MRI LME in MS and other neurological diseases. MATERIALS AND METHODS In a comprehensive literature search in Medline, Scopus, and Embase, out of 2292 publications, 459 records assessing LME in neurological diseases were eligible for qualitative synthesis. Of these, 135 were included in a random-effects model meta-analysis with subgroup analyses for MS. RESULTS Of eligible publications, 161 investigated LME in neoplastic neurological (n = 2392), 91 in neuroinfectious (n = 1890), and 75 in primary neuroinflammatory diseases (n = 4038). The LME-proportions for these disease classes were 0.47 [95%-CI: 0.37-0.57], 0.59 [95%-CI: 0.47-0.69], and 0.26 [95%-CI: 0.20-0.35], respectively. In a subgroup analysis comprising 1605 MS cases, LME proportion was 0.30 [95%-CI 0.21-0.42] with lower proportions in relapsing-remitting (0.19 [95%-CI 0.13-0.27]) compared to progressive MS (0.39 [95%-CI 0.30-0.49], p = 0.002) and higher proportions in studies imaging at 7 T (0.79 [95%-CI 0.64-0.89]) compared to lower field strengths (0.21 [95%-CI 0.15-0.29], p < 0.001). LME in MS was associated with longer disease duration (mean difference 2.2 years [95%-CI 0.2-4.2], p = 0.03), higher Expanded Disability Status Scale (mean difference 0.6 points [95%-CI 0.2-1.0], p = 0.006), higher T1 (mean difference 1.6 ml [95%-CI 0.1-3.0], p = 0.04) and T2 lesion load (mean difference 5.9 ml [95%-CI 3.2-8.6], p < 0.001), and lower cortical volume (mean difference -21.3 ml [95%-CI -34.7--7.9], p = 0.002). CONCLUSIONS Our study provides high-grade evidence for the substantial presence of LME in MS and a comprehensive panel of other neurological diseases. Our data could facilitate differential diagnosis of LME in clinical settings. Additionally, our meta-analysis corroborates that LME is associated with key clinical and imaging features of MS. PROSPERO No: CRD42021235026

Super resolution using sparse sampling at portable ultra-low field MR

Ultra-low field (ULF) magnetic resonance imaging (MRI) holds the potential to make MRI more accessible, given its cost-effectiveness, reduced power requirements, and portability. However, signal-to-noise ratio (SNR) drops with field strength, necessitating imaging with lower resolution and longer scan times. This study introduces a novel Fourier-based Super Resolution (FouSR) approach, designed to enhance the resolution of ULF MRI images with minimal increase in total scan time. FouSR combines spatial frequencies from two orthogonal ULF images of anisotropic resolution to create an isotropic T2-weighted fluid-attenuated inversion recovery (FLAIR) image. We hypothesized that FouSR could effectively recover information from under-sampled slice directions, thereby improving the delineation of multiple sclerosis (MS) lesions and other significant anatomical features. Importantly, the FouSR algorithm can be implemented on the scanner with changes to the k-space trajectory. Paired ULF (Hyperfine SWOOP, 0.064 tesla) and high field (Siemens, Skyra, 3 Tesla) FLAIR scans were collected on the same day from a phantom and a cohort of 10 participants with MS or suspected MS (6 female; mean ± SD age: 44.1 ± 4.1). ULF scans were acquired along both coronal and axial planes, featuring an in-plane resolution of 1.7 mm × 1.7 mm with a slice thickness of 5 mm. FouSR was evaluated against registered ULF coronal and axial scans, their average (ULF average) and a gold standard SR (ANTs SR). FouSR exhibited higher SNR (47.96 ± 12.6) compared to ULF coronal (36.7 ± 12.2) and higher lesion conspicuity (0.12 ± 0.06) compared to ULF axial (0.13 ± 0.07) but did not exhibit any significant differences contrast-to-noise-ratio (CNR) compared to other methods in patient scans. However, FouSR demonstrated superior image sharpness (0.025 ± 0.0040) compared to all other techniques (ULF coronal 0.021 ± 0.0037, q = 5.9, p-adj. = 0.011; ULF axial 0.018 ± 0.0026, q = 11.1, p-adj. = 0.0001; ULF average 0.019 ± 0.0034, q = 24.2, p-adj. < 0.0001) and higher lesion sharpness (−0.97 ± 0.31) when compared to the ULF average (−1.02 ± 0.37, t(543) = −10.174, p = <0.0001). Average blinded qualitative assessment by three experienced MS neurologists showed no significant difference in WML and sulci or gyri visualization between FouSR and other methods. FouSR can, in principle, be implemented on the scanner to produce clinically useful FLAIR images at higher resolution on the fly, providing a valuable tool for visualizing lesions and other anatomical structures in MS

Towards contrast-agnostic soft segmentation of the spinal cord

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants ($\text{n}=267$; $\text{contrasts}=6$), we first generated participant-wise soft ground truth (GT) by averaging the binary segmentations across all 6 contrasts. These soft GT, along with a regression-based loss function, were then used to train a UNet model for spinal cord segmentation. We evaluated our model against state-of-the-art methods and performed ablation studies involving different GT mask types, loss functions, and contrast-specific models. Our results show that using the soft average segmentations along with a regression loss function reduces CSA variability ($p < 0.05$, Wilcoxon signed-rank test). The proposed spinal cord segmentation model generalizes better than the state-of-the-art contrast-specific methods amongst unseen datasets, vendors, contrasts, and pathologies (compression, lesions), while accounting for partial volume effects.Comment: Submitted to Medical Image Analysi

Harmonizing Definitions for Progression Independent of Relapse Activity in Multiple Sclerosis: A Systematic Review

IMPORTANCE: Emerging evidence suggests that progression independent of relapse activity (PIRA) is a substantial contributor to long-term disability accumulation in relapsing-remitting multiple sclerosis (RRMS). To date, there is no uniform agreed-upon definition of PIRA, limiting the comparability of published studies. OBJECTIVE: To summarize the current evidence about PIRA based on a systematic review, to discuss the various terminologies used in the context of PIRA, and to propose a harmonized definition for PIRA for use in clinical practice and future trials. EVIDENCE REVIEW: A literature search was conducted using the search terms multiple sclerosis, PIRA, progression independent of relapse activity, silent progression, and progression unrelated to relapses in PubMed, Embase, Cochrane, and Web of Science, published between January 1990 and December 2022. FINDINGS: Of 119 identified single records, 48 eligible studies were analyzed. PIRA was reported to occur in roughly 5% of all patients with RRMS per annum, causing at least 50% of all disability accrual events in typical RRMS. The proportion of PIRA vs relapse-associated worsening increased with age, longer disease duration, and, despite lower absolute event numbers, potent suppression of relapses by highly effective disease-modifying therapy. However, different studies used various definitions of PIRA, rendering the comparability of studies difficult. CONCLUSION AND RELEVANCE: PIRA is the most frequent manifestation of disability accumulation across the full spectrum of traditional MS phenotypes, including clinically isolated syndrome and early RRMS. The harmonized definition suggested here may improve the comparability of results in current and future cohorts and data sets

Levels of brain-derived neurotrophic factor in patients with multiple sclerosis

Objective To determine the levels of brain‐derived neurotrophic factor (BDNF) in the serum of patients suffering from multiple sclerosis (MS) to evaluate the potential of serum BDNF as a biomarker for MS. Methods Using a recently validated enzyme‐linked immunoassay (ELISA) we measured BDNF in patients with MS (pwMS), diagnosed according to the 2001 McDonald criteria and aged between 18 and 70 years, participating in a long‐term cohort study with annual clinical visits, including blood sampling, neuropsychological testing, and brain magnetic resonance imaging (MRI). The results were compared with an age‐ and sex‐matched cohort of healthy controls (HC). Correlations between BDNF levels and a range of clinical and magnetic resonance imaging variables were assessed using an adjusted linear model. Results In total, 259 pwMS and 259 HC were included, with a mean age of 44.42 ± 11.06 and 44.31 ± 11.26 years respectively. Eleven had a clinically isolated syndrome (CIS), 178 relapsing remitting MS (RRMS), 56 secondary progressive MS (SPMS), and 14 primary progressive MS (PPMS). Compared with controls, mean BDNF levels were lower by 8 % (p˂0.001) in pwMS. The level of BDNF in patients with SPMS was lower than in RRMS (p = 0.004). Interpretation We conclude that while the use of comparatively large cohorts enables the detection of a significant difference in BDNF levels between pwMS and HC, the difference is small and unlikely to usefully inform decision‐making processes at an individual patient level

Pseudo-Label Assisted nnU-Net enables automatic segmentation of 7T MRI from a single acquisition

IntroductionAutomatic whole brain and lesion segmentation at 7T presents challenges, primarily from bias fields, susceptibility artifacts including distortions, and registration errors. Here, we sought to use deep learning algorithms (D/L) to do both skull stripping and whole brain segmentation on multiple imaging contrasts generated in a single Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) acquisition on participants clinically diagnosed with multiple sclerosis (MS), bypassing registration errors.MethodsBrain scans Segmentation from 3T and 7T scanners were analyzed with software packages such as FreeSurfer, Classification using Derivative-based Features (C-DEF), nnU-net, and a novel 3T-to-7T transfer learning method, Pseudo-Label Assisted nnU-Net (PLAn). 3T and 7T MRIs acquired within 9 months from 25 study participants with MS (Cohort 1) were used for training and optimizing. Eight MS patients (Cohort 2) scanned only at 7T, but with expert annotated lesion segmentation, was used to further validate the algorithm on a completely unseen dataset. Segmentation results were rated visually by experts in a blinded fashion and quantitatively using Dice Similarity Coefficient (DSC).ResultsOf the methods explored here, nnU-Net and PLAn produced the best tissue segmentation at 7T for all tissue classes. In both quantitative and qualitative analysis, PLAn significantly outperformed nnU-Net (and other methods) in lesion detection in both cohorts. PLAn's lesion DSC improved by 16% compared to nnU-Net.DiscussionLimited availability of labeled data makes transfer learning an attractive option, and pre-training a nnUNet model using readily obtained 3T pseudo-labels was shown to boost lesion detection capabilities at 7T

Acute Polyradiculomyelitis With Spinal Cord Gray Matter Lesions: A Report of Two Cases

Objective: Inflammatory polyradiculomyelitis belongs to a rare group of immune-mediated diseases affecting both the central and peripheral nervous system. We aimed to describe an unusual presentation of acute polyradiculomyelitis with marked spinal cord lesions restricted to the gray matter. Methods: Thorough examination of two case reports including clinical, MRI, serologic, electrophysiologic and CSF examinations as well as short-term follow-up. Results: We present two adult patients with acute polyradiculomyelitis and unusual spinal cord lesions restricted to the gray matter on MRI. The clinical presentation, serologic, electrophysiologic and CSF features of the two patients varied, whereas both patients demonstrated severe, asymmetrical, predominantly distal, motor deficits of the lower extremities as well as bladder and bowel dysfunction. Both patients only partially responded to anti-inflammatory treatment. Severe motor impairment and bladder dysfunction persisted even months after symptom onset. Conclusions: To our best of knowledge, these are the first reports of acute polyradiculomyelitis with distinct involvement of the lower thoracic spinal cord gray matter. Currently, it remains unclear whether gray matter lesions reflect a separate pathophysiologic mechanism or an exceedingly rare presentation of spinal cord involvement in acute polyradiculomyelitis