Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI

The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention. Accurate detection of the small and scarce lesions requires specialized sequences and high or ultra- high field MRI. For supervised training based on multimodal structural MRI at 7T, two experts generated ground truth segmentation masks of 60 patients with 2014 CLs. We implemented a simplified 3D U-Net with three resolution levels (3D U-Net-). By increasing the complexity of the task (adding brain tissue segmentation), while randomly dropping input channels during training, we improved the performance compared to the baseline. Considering a minimum lesion size of 0.75 μL, we achieved a lesion-wise cortical lesion detection rate of 67% and a false positive rate of 42%. However, 393 (24%) of the lesions reported as false positives were post-hoc confirmed as potential or definite lesions by an expert. This indicates the potential of the proposed method to support experts in the tedious process of CL manual segmentation

Ultra-high field 7 T imaging in multiple sclerosis.

Purpose of review: Ultra-high field 7 T MRI has multiple applications for the in vivo characterization of the heterogeneous aspects underlying multiple sclerosis including the identification of cortical lesions, characterization of the different types of white matter plaques, evaluation of structures difficult to assess with conventional MRI (thalamus, cerebellum, spinal cord, meninges). Recent findings: The sensitivity of cortical lesion detection at 7 T is twice than at lower field MRI, especially for subpial lesions, the most common cortical lesion type in multiple sclerosis. Cortical lesion load accrual is independent of that in the white matter and predicts disability progression.Seven Tesla MRI provides details on tissue microstructure that can be used to improve white matter lesion characterization. These include the presence of a central vein, whose identification can be used to improve multiple sclerosis diagnosis, or the appearance of an iron-rich paramagnetic rim on susceptibility-weighted images, which corresponds to iron-rich microglia at the periphery of slow expanding lesions. Improvements in cerebellar and spinal cord tissue delineation and lesion characterization have also been demonstrated. Summary: Imaging at 7 T allows assessing more comprehensively the complementary pathophysiological aspects of multiple sclerosis, opening up novel perspectives for clinical and therapeutics evaluation

Evidence for Progressive Microstructural Damage in Early Multiple Sclerosis by Multi-Shell Diffusion Magnetic Resonance Imaging

In multiple sclerosis (MS), it would be of clinical value to be able to track the progression of axonal pathology, especially before the manifestation of clinical disability. However, non-invasive evaluation of short-term longitudinal progression of white matter integrity is challenging. This study aims at assessing longitudinal changes in the restricted (i.e. intracellular) diffusion signal fraction (FR) in early-stage MS by using ultra-high gradient strength multi-shell diffusion magnetic resonance imaging. In 11 early MS subjects (disease duration ≤ 5 years), FR was obtained at two timepoints (one year apart) through the Composite Hindered and Restricted Model of Diffusion, along with conventional Diffusion Tensor Imaging metrics. At follow-up, no statistically significant change was detected in clinical variables, while all imaging metrics showed statistically significant longitudinal changes (p < 0.01, corrected for multiple comparisons) in widespread regions in normal-appearing white matter (NAWM). The most extensive longitudinal changes were observed in FR, including areas known to include a large fraction of crossing fibers. Furthermore, FR was also the only metric showing significant longitudinal changes in lesions that were present at both time points (p = 0.007), with no significant differences found for conventional diffusion metrics. Finally, FR was the only diffusion metric (as compared to Diffusion Tensor Imaging) that revealed pre-lesional changes already present at baseline. Taken together, our data provide evidence for progressive microstructural damage in the NAWM of early MS cases detectable already at 1-year follow-up. Our study highlights the value of multi-shell diffusion imaging for sensitive tracking of disease evolution in MS before any clinical changes are observed. This article is part of a Special Issue entitled: SI: MRI and Neuroinflammation

Whole brain in vivo axonal diameter mapping in multiple sclerosis

Traditional techniques based on diffusion MR imaging suffer from extremely low specificity in separating disease-related alterations in white matter microstructure, which can involve multiple phenomena including axonal loss, demyelination and changes in axonal size. Multi-shell diffusion MRI is able to greatly increase specificity by concomitantly exploring multiple diffusion timescales. If multi-shell acquisition is combined with an exploration of different diffusion times, diffusion data allows the estimation of sophisticated compartmental models, which provide greatly enhanced specificity to the presence of different tissue sub-compartments, as well as estimates of intra-voxel axonal diameter distributions. In this paper, we apply a multiple-b-value, high angular resolution multi-shell diffusion MRI protocol with varying diffusion times to a cohort of multiple sclerosis (MS) patients and compare them to a population of healthy controls. By fitting the AxCaliber model, we are able to extract indices for axonal diameter across the whole brain. We show that MS is associated with widespread increases of axonal diameter and that our axonal diameter estimation provides the highest discrimination power for local alterations in normal-appearing white matter in MS compared to controls. AxCaliber has the potential to disentangle microstructural alterations in MS and holds great promises to become a sensitive and specific non-invasive biomarker of irreversible disease progression

Early axonal damage in normal appearing white matter in multiple sclerosis: Novel insights from multi-shell diffusion MRI

Conventional diffusion-weighted MR imaging techniques provide limited specificity in disentangling disease-related microstructural alterations involving changes in both axonal density and myelination. By simultaneously probing multiple diffusion regimens, multi-shell diffusion MRI is capable of increasing specificity to different tissue sub-compartments and hence separate different contributions to changes in diffusion-weighted signal attenuation. Advanced multi-shell diffusion models impose significant requirements on the amount of diffusion weighting (i.e. gradient coil performance) and angular resolution (i.e. in-scanner subject time), which commonly limits their applicability in a clinical setting. In this paper, we apply a high-b-value, high angular resolution multi-shell diffusion MRI protocol to a population of early multiple sclerosis (MS) patients and healthy controls. Through the Composite Hindered and Restricted Model of Diffusion (CHARMED) model, we extract indices for axonal density as well as parameters sensitive to myelin. We demonstrate increased sensitivity to microstructural changes in normal appearing white matter and in lesions in MS as compared to traditional models like DTI. These changes appear to be predominantly in axonal density, pointing towards the existence of axonal damage mechanisms in early MS

Characterization of thalamic lesions and their correlates in multiple sclerosis by ultra-high-field MRI.

Background: Thalamic pathology is a marker for neurodegeneration and multiple sclerosis (MS) disease progression. Objective: To characterize (1) the morphology of thalamic lesions, (2) their relation to cortical and white matter (WM) lesions, and (3) clinical measures, and to assess (4) the imaging correlates of thalamic atrophy. Methods: A total of 90 MS patients and 44 healthy controls underwent acquisition of 7 Tesla images for lesion segmentation and 3 Tesla scans for atrophy evaluation. Thalamic lesions were classified according to the shape and the presence of a central venule. Regression analysis identified the predictors of (1) thalamic atrophy, (2) neurological disability, and (3) information processing speed. Results: Thalamic lesions were mostly ovoid than periventricular, and for the great majority (78%) displayed a central venule. Lesion volume in the thalamus, cortex, and WM did not correlate with each other. Thalamic atrophy was only associated with WM lesion volume (p = 0.002); subpial and WM lesion volumes were associated with neurological disability (p = 0.016; p < 0.001); and WM and thalamic lesion volumes were related with cognitive impairment (p < 0.001; p = 0.03). Conclusion: Thalamic lesions are unrelated to those in the cortex and WM, suggesting that they may not share common pathogenic mechanisms and do not contribute to thalamic atrophy. Combined WM, subpial, and thalamic lesion volumes at 7 Tesla contribute to the disease severity

Microglia activation in cerebellum increases with proximity to the fourth ventricle in progressive MS.

Text: Introduction. Cerebellar pathology contributes to disease progression in multiple sclerosis (MS). Neuroimaging studies show that demyelination in the normal appearing brain and cerebellum tends to occur mainly close to the inner (periventricular) and/or outer (subpial) surfaces, possibly driven by cerebro-spinal fluid inflammatory factors. Aims. To investigate the periventricular distribution of neuroinflammat ion in the cerebellum in relapsing remitting MS (RRMS) and secondary progressive MS (SPMS) relative to healthy controls (HC) using integrated 3 Tesla Magnetic Resonance/Positron Emission Tomography (MR-PET) with C-PBR28, a tracer for activated microglia. Methods. Sixteen RRMS, 15 SPMS and 16 HC underwent 90' C-PBR28 MR-PET scan to obtain 60-90' standardized uptake values normalized by a pseudo-reference region (SUVR), at different distances from the IV ventricle. Fourth ventricle masks were segmented with Freesurfer from anatomical T1 images and concentric periventricular slices were extracted from normal appearing cerebellar tissue underlying the cerebellar cortex at 3-6, 6-9 and 9-12 mm from the IV ventricle. To avoid partial volume effects, the first slice extending 0 to 3 mm from the ventricle was excluded. Mean SUVR values from each slice were obtained with FSL in RRMS, SPMS and HC. Matched pairs t-test was used to estimate uptake differences among the three slices in each group. Multiple linear regression was applied to compare tracer uptake at similar distance among the three groups, age and radiotracer binding affinity being covariates of no interest. Results. Each group (RRMS, SPMS, HC) showed a gradient in PBR SUVR decreasing from the IV ventricle towards the cortex (p< 0.05). At similar distance from IV ventricle, a significant difference in SUVR was present only when comparing SPMS to HC. This difference was more marked close to the IV ventricle (p= 0.03 at 3-6 mm, p= 0.04 at 6-9 mm, p= 0.05 at 9-12 mm). This pattern was present also when comparing SPMS to RRMS though the uptake did not significantly differ between them. Conclusion. Cerebellar C-PBR 28 tracer uptake showed, relative to HC, a mild decreasing gradient from the IV ventricle in SPMS but not in RRMS. This finding suggests that neuroinflammation, although diffuse, tends to be higher near the inner cerebellar surface, at least in progressive disease. Further investigation is needed to clarify the role of neuroinflammation in the pathogenesis of cerebellar demyelination

The relevance of multiple sclerosis cortical lesions on cortical thinning and their clinical impact as assessed by 7.0-T MRI.

Objective: This study aimed to investigate at 7.0-T MRI a) the role of multiple sclerosis (MS) cortical lesions in cortical tissue loss b) their relation to neurological disability. Methods: In 76 relapsing remitting and 26 secondary progressive MS patients (N = 102) and 56 healthy subjects 7.0-T T2*-weighted images were acquired for lesion segmentation; 3.0-T T1-weighted structural scans for cortical surface reconstruction/cortical thickness estimation. Patients were dichotomized based on the median cortical lesion volume in low and high cortical lesion load groups that differed by age, MS phenotype and degree of neurological disability. Group differences in cortical thickness were tested on reconstructed cortical surface. Patients were evaluated clinically by means of the Expanded Disability Status Scale (EDSS). Results: Cortical lesions were detected in 96% of patients. White matter lesion load was greater in the high than in the low cortical lesion load MS group (p = 0.01). Both MS groups disclosed clusters (prevalently parietal) of cortical thinning relative to healthy subjects, though these regions did not show the highest cortical lesion density, which predominantly involved frontal regions. Cortical thickness decreased on average by 0.37 mm, (p = 0.002) in MS patients for each unit standard deviation change in white matter lesion volume. The odds of having a higher EDSS were associated with cortical lesion volume (1.78, p = 0.01) and disease duration (1.15, p &lt; 0.001). Conclusion: Cortical thinning in MS is not directly related to cortical lesion load but rather with white matter lesion volume. Neurological disability in MS is better explained by cortical lesion volume assessment

7 T imaging reveals a gradient in spinal cord lesion distribution in multiple sclerosis.

We used 7 T MRI to: (i) characterize the grey and white matter pathology in the cervical spinal cord of patients with early relapsing-remitting and secondary progressive multiple sclerosis; (ii) assess the spinal cord lesion spatial distribution and the hypothesis of an outside-in pathological process possibly driven by CSF-mediated immune cytotoxic factors; and (iii) evaluate the association of spinal cord pathology with brain burden and its contribution to neurological disability. We prospectively recruited 20 relapsing-remitting, 15 secondary progressive multiple sclerosis participants and 11 age-matched healthy control subjects to undergo 7 T imaging of the cervical spinal cord and brain as well as conventional 3 T brain acquisition. Cervical spinal cord imaging at 7 T was used to segment grey and white matter, including lesions therein. Brain imaging at 7 T was used to segment cortical and white matter lesions and 3 T imaging for cortical thickness estimation. Cervical spinal cord lesions were mapped voxel-wise as a function of distance from the inner central canal CSF pool to the outer subpial surface. Similarly, brain white matter lesions were mapped voxel-wise as a function of distance from the ventricular system. Subjects with relapsing-remitting multiple sclerosis showed a greater predominance of spinal cord lesions nearer the outer subpial surface compared to secondary progressive cases. Inversely, secondary progressive participants presented with more centrally located lesions. Within the brain, there was a strong gradient of lesion formation nearest the ventricular system that was most evident in participants with secondary progressive multiple sclerosis. Lesion fractions within the spinal cord grey and white matter were related to the lesion fraction in cerebral white matter. Cortical thinning was the primary determinant of the Expanded Disability Status Scale, white matter lesion fractions in the spinal cord and brain of the 9-Hole Peg Test and cortical thickness and spinal cord grey matter cross-sectional area of the Timed 25-Foot Walk. Spinal cord lesions were localized nearest the subpial surfaces for those with relapsing-remitting and the central canal CSF surface in progressive disease, possibly implying CSF-mediated pathogenic mechanisms in lesion development that may differ between multiple sclerosis subtypes. These findings show that spinal cord lesions involve both grey and white matter from the early multiple sclerosis stages and occur mostly independent from brain pathology. Despite the prevalence of cervical spinal cord lesions and atrophy, brain pathology seems more strongly related to physical disability as measured by the Expanded Disability Status Scale

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox