Josefina Maranzano

Evaluation of automated techniques for the quantification of grey matter atrophy in patients with multiple sclerosis

Several methods exist and are frequently used to quantify grey matter (GM) atrophy in multiple sclerosis (MS). Fundamental to all available techniques is the accurate segmentation of GM in the brain, a difficult task confounded even further by the pathology present in the brains of MS patients. In this paper, we examine the segmentations of six different automated techniques and compare them to a manually defined reference standard. Results demonstrate that, although the algorithms perform similarly to manual segmentations of cortical GM, severe shortcomings are present in the segmentation of deep GM structures. This deficiency is particularly relevant given the current interest in the role of GM in MS and the numerous reports of atrophy in deep GM structures.

Manual Segmentation of MS Cortical Lesions Using MRI: A Comparison of 3 MRI Reading Protocols

BACKGROUND AND PURPOSE: Double inversion recovery has been suggested as the MR imaging contrast of choice for segmenting cortical lesions in patients with multiple sclerosis. In this study, we sought to determine the utility of double inversion recovery for cortical lesion identification by comparing 3 MR imaging reading protocols that combine different MR imaging contrasts. MATERIALS AND METHODS: Twenty-five patients with relapsing-remitting MS and 3 with secondary-progressive MS were imaged with 3T MR imaging by using double inversion recovery, dual fast spin-echo proton-density/T2-weighted, 3D FLAIR, and 3D T1-weighted imaging sequences. Lesions affecting the cortex were manually segmented by using the following 3 MR imaging reading protocols: Protocol 1 (P1) used all available MR imaging contrasts; protocol 2 (P2) used all the available contrasts except for double inversion recovery; and protocol 3(P3) used only double inversion recovery. RESULTS: Six hundred forty-three cortical lesions were identified with P1 (mean = 22.96); 633, with P2 (mean = 22.6); and 280, with P3 (mean = 10). The counts obtained by using P1 and P2 were not significantly different (P = .93). The counts obtained by using P3 were significantly smaller than those obtained by using either P1 (P < .001) or P2 (P < .001). The intraclass correlation coefficients were P1 versus P2 = 0.989, P1 versus P3 = 0.615, and P2 versus P3 = 0.588. CONCLUSIONS: MR imaging cortical lesion segmentation can be performed by using 3D T1-weighted and 3D FLAIR images acquired with a 1-mm isotropic voxel size, supported by conventional T2-weighted and proton-density images with 3-mm-thick sections. Inclusion of double inversion recovery in this multimodal reading protocol did not significantly improve the cortical lesion identification rate. A multimodal approach is superior to using double inversion recovery alone.

MRI evidence of acute inflammation in leukocortical lesions of patients with early multiple sclerosis.

Objective: To identify gadolinium-enhancing lesions affecting the cortex of patients with early multiple sclerosis (MS) and to describe the frequency and evolution of these lesions. Methods: We performed a retrospective, observational, longitudinal analysis of MRI scans collected as part of the Betaseron vs Copaxone in Multiple Sclerosis with Triple-Dose Gadolinium and 3T MRI Endpoints (BECOME) study. Seventy-five patients with early-stage MS were scanned monthly, over a period of 12–24 months, using 3T MRI after administration of triple-dose gadolinium. A total of 1,188 scans were included in the analysis. A total of 139 were selected using an image pipeline algorithm that integrated the image information from cortical gray matter masks and gadolinium-enhancing lesion masks. These scans were evaluated to identify gadolinium-enhancing lesions affecting the cortex. Results: The total number of gadolinium-enhancing lesions was 2,044. The number of gadolinium-enhancing lesions affecting the cortex was 120 (6%), 95% of which were leukocortical. The number of patients who showed gadolinium-enhancing lesions affecting the cortex was 27 (36%). The number of gadolinium-enhancing lesions affecting the cortex at baseline was 25 (21%) and the number of new lesions that developed in follow-up scans was 49 (41%). The number of persistent lesions was 46 (38%). Conclusions: The presence of enhancing lesions affecting the cortex and adjacent white matter, although transient and not frequent, suggests that at least some cortical lesions are related to blood–brain barrier disruption. Our data support the concept that there may be an acute inflammatory phase in the development of leukocortical MS lesions. Clinicaltrials.gov identifier: NCT00176592.

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

Introduction: White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimers disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter‐rater and intra‐rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown. Methods: We compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co‐registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi‐site datasets, including images acquired with different scanners and different scan‐parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naïve Bayes, logistic regression, decision trees, random forests, support vector machines, k‐nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel‐wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra‐Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open‐source science, we also make available a fully automated tool for segmentation of WMHs with pre‐trained classifiers for all these techniques. Results: Random Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0.66±0.17 and ICC=0.99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0.72±0.10, ICC=0.93 for the NACC dataset (using T1w and FLAIR scans), SI=0.66±0.23, ICC=0.94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0.72±0.19, ICC=0.96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0.66±0.17, ICC=0.99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0.47±0.21, ICC=0.95). Conclusion: Our investigations showed that with appropriate features, most off‐the‐shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.

Delineation of cortical pathology in multiple sclerosis using multi-surface magnetization transfer ratio imaging

The purpose of our study was to evaluate the utility of measurements of cortical surface magnetization transfer ratio (csMTR) on the inner, mid and outer cortical boundaries as clinically accessible biomarkers of cortical gray matter pathology in multiple sclerosis (MS). Twenty-five MS patients and 12 matched controls were recruited from the MS Clinic of the Montreal Neurological Institute. Anatomical and magnetization transfer ratio (MTR) images were acquired using 3 Tesla MRI at baseline and two-year time-points. MTR maps were smoothed along meshes representing the inner, mid and outer neocortical boundaries. To evaluate csMTR reductions suggestive of sub-pial demyelination in MS patients, a mixed model analysis was carried out at both the individual vertex level and in anatomically parcellated brain regions. Our results demonstrate that focal areas of csMTR reduction are most prevalent along the outer cortical surface in the superior temporal and posterior cingulate cortices, as well as in the cuneus and precentral gyrus. Additionally, age regression analysis identified that reductions of csMTR in MS patients increase with age but appear to hit a plateau in the outer caudal anterior cingulate, as well as in the precentral and postcentral cortex. After correction for the naturally occurring gradient in cortical MTR, the difference in csMTR between the inner and outer cortex in focal areas in the brains of MS patients correlated with clinical disability. Overall, our findings support multi-surface analysis of csMTR as a sensitive marker of cortical sub-pial abnormality indicative of demyelination in MS patients.

White matter hyperintensities are linked to future cognitive decline in de novo Parkinson's disease patients

White Matter Hyperintensities (WMHs) are associated with cognitive decline in aging and Alzheimers disease. However, the pathogenesis of cognitive decline in Parkinsons disease (PD) is not as clearly related to vascular causes, and therefore the role of WMHs as a marker of small-vessel disease (SVD) in PD is less clear. Currently, SVD in PD is assessed and treated independently of the disease. However, if WMH as the major MRI sign of SVD has a higher impact on cognitive decline in PD patients than in healthy controls, vascular pathology needs to be assessed and treated with a higher priority in this population. Here we investigate whether the presence of WMHs leads to increased cognitive decline in de novo PD, and if these effects relate to cortical atrophy. WMHs and cortical thickness were measured in de novo PD patients and age-matched controls (NPD = 365, NControl = 174) from Parkinsons Progression Markers Initiative (PPMI) to study the relationship between baseline WMHs, future cognitive decline (follow-up: 4.09 ± 1.14 years) and cortical atrophy (follow-up: 1.05 ± 0.10 years). PD subjects with high baseline WMH loads had significantly greater cognitive decline than i) PD subjects with low WMH load, and ii) control subjects with high WMH load. Furthermore, in PD subjects, high WMH load resulted in more cortical thinning in the right frontal lobe. Theses results show that the presence of WMHs in de novo PD patients predicts greater future cognitive decline and cortical atrophy than in normal aging.

Validation of T1w-based segmentations of white matter hyperintensity volumes in large-scale datasets of aging

Fluid‐attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimers disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints.

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

&NA; 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.

Detection and clinical correlation of leukocortical lesions in pediatric-onset multiple sclerosis on multi-contrast MRI

Objective: To determine the frequency of cortical lesions (CLs) in patients with pediatric-onset multiple sclerosis (POMS) using multi-contrast magnetic resonance imaging (MRI), and the relationship between frontal CL load and upper limb dexterity assessed with the Nine-Hole Peg Test (9-HPT). Methods: Participants completed the 9-HPT and were imaged on a 3T MRI scanner to collect T1-weighted three-dimensional (3D) magnetization prepared rapid gradient echo (MPRAGE), proton density–weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) images. CLs were manually segmented using all MRI contrasts. Results: We enrolled 24 participants with POMS (mean (standard deviation) age at first symptom: 13.3 (±2.7) years; mean age at scan: 18.8 (±3) years; mean disease duration of 5 (±3.2) years). A total of 391 CLs (mean, 16.3 ± 27.2; median, 7) were identified in 19 of 24 POMS patients (79%). The total number of CLs was positively associated with white matter lesion volume (p = 0.04) but not with thalamic volume, age at the time of the scan, or disease duration. The number of frontal CLs was associated with slower performance on the 9-HPT (p = 0.05). Conclusion: Multi-contrast 3T MRI led to a high rate of CL detection, demonstrating that cortical pathology occurs even in pediatric-onset disease. Frontal lobe CL count was associated with reduced manual dexterity, indicating that these CLs are clinically relevant.

Focal and diffuse cervical spinal cord damage in patients with early relapsing–remitting MS: A multicentre magnetisation transfer ratio study:

Background: Studies including patients with well-established multiple sclerosis (MS) have shown a significant and disability-related reduction in the cervical spinal cord (SC) magnetisation transfer ratio (MTR). Objectives: The objectives are to (1) assess whether MTR reduction is already measurable in the SC of patients with early relapsing–remitting multiple sclerosis (RRMS) and (2) describe its spatial distribution. Methods: We included 60 patients with RRMS <12  months and 34 age-matched controls at five centres. Axial T2*w, sagittal T2w, sagittal phase-sensitive inversion recovery (PSIR), 3DT1w, and axial magnetisation transfer (MT) images were acquired from C1 to C7. Lesions were manually labelled and mean MTR values computed both for the whole SC and for normal-appearing SC in different regions of interest. Results: Mean whole SC MTR was significantly lower in patients than controls (33.7 vs 34.9  pu, p  =  0.00005), even after excluding lesions (33.9  pu, p  =  0.0003). We observed a greater mean reduction in MTR for vertebral levels displaying the highest lesion loads (C2–C4). In the axial plane, we observed a greater mean MTR reduction at the SC periphery and barycentre. Conclusion: Cervical SC tissue damage measured using MTR is not restricted to macroscopic lesions in patients with early RRMS and is not homogeneously distributed.