Daniel S. Reich

YNIMG Journal 2023 Journal Article

Effect of motion, cortical orientation and spatial resolution on quantitative imaging of cortical R2* and magnetic susceptibility at 0.3 mm in-plane resolution at 7 T

• Peter van Gelderen
• Xu Li
• Jacco A. de Zwart
• Erin S. Beck
• Serhat V. Okar
• Yujia Huang
• KuoWei Lai
• Jeremias Sulam

MR images of the effective relaxation rate R2* and magnetic susceptibility χ derived from multi-echo T2*-weighted (T2*w) MRI can provide insight into iron and myelin distributions in the brain, with the potential of providing biomarkers for neurological disorders. Quantification of R2* and χ at submillimeter resolution in the cortex in vivo has been difficult because of challenges such as head motion, limited signal to noise ratio, long scan time, and motion related magnetic field fluctuations. This work aimed to improve the robustness for quantifying intracortical R2* and χ and analyze the effects from motion, spatial resolution, and cortical orientation. T2*w data was acquired with a spatial resolution of 0. 3 × 0. 3 × 0. 4 mm3 at 7 T and downsampled to various lower resolutions. A combined correction for motion and B0 changes was deployed using volumetric navigators. Such correction improved the T2*w image quality rated by experienced image readers and test-retest reliability of R2* and χ quantification with reduced median inter-scan differences up to 10 s−1 and 5 ppb, respectively. R2* and χ near the line of Gennari, a cortical layer high in iron and myelin, were as much as 10 s−1 and 10 ppb higher than the region at adjacent cortical depth. In addition, a significant effect due to the cortical orientation relative to the static field (B0) was observed in χ with a peak-to-peak amplitude of about 17 ppb. In retrospectively downsampled data, the capability to distinguish different cortical depth regions based on R2* or χ contrast remained up to isotropic 0. 5 mm resolution. This study highlights the unique characteristics of R2* and χ along the cortical depth at submillimeter resolution and the need for motion and B0 corrections for their robust quantification in vivo.

YNICL Journal 2022 Journal Article

From pathology to MRI and back: Clinically relevant biomarkers of multiple sclerosis lesions

• Hadar Kolb
• Omar Al-Louzi
• Erin S. Beck
• Pascal Sati
• Martina Absinta
• Daniel S. Reich

Focal lesions in both white and gray matter are characteristic of multiple sclerosis (MS). Histopathological studies have helped define the main underlying pathological processes involved in lesion formation and evolution, serving as a gold standard for many years. However, histopathology suffers from an intrinsic bias resulting from over-reliance on tissue samples from late stages of the disease or atypical cases and is inadequate for routine patient assessment. Pathological-radiological correlative studies have established advanced MRI's sensitivity to several relevant MS-pathological substrates and its practicality for assessing dynamic changes and following lesions over time. This review focuses on novel imaging techniques that serve as biomarkers of critical pathological substrates of MS lesions: the central vein, chronic inflammation, remyelination and repair, and cortical lesions. For each pathological process, we address the correlative value of MRI to MS pathology, its contribution in elucidating MS pathology in vivo, and the clinical utility of the imaging biomarker.

YNICL Journal 2022 Journal Article

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

• Benjamin V. Ineichen
• Charidimos Tsagkas
• Martina Absinta
• Daniel S. Reich

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.

YNICL Journal 2022 Journal Article

Sensitivity of portable low-field magnetic resonance imaging for multiple sclerosis lesions

• T. Campbell Arnold
• Danni Tu
• Serhat V. Okar
• Govind Nair
• Samantha By
• Karan D. Kawatra
• Timothy E. Robert-Fitzgerald
• Lisa M. Desiderio

Magnetic resonance imaging (MRI) is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low-field strength, MRI scanners could potentially lower financial and technical barriers to neuroimaging and reach underserved or disabled populations, but the sensitivity of these devices for MS lesions is unknown. We sought to determine if white matter lesions can be detected on a portable 64mT scanner, compare automated lesion segmentations and total lesion volume between paired 3T and 64mT scans, identify features that contribute to lesion detection accuracy, and explore super-resolution imaging at low-field. In this prospective, cross-sectional study, same-day brain MRI (FLAIR, T1w, and T2w) scans were collected from 36 adults (32 women; mean age, 50 ± 14 years) with known or suspected MS using Siemens 3T (FLAIR: 1 mm isotropic, T1w: 1 mm isotropic, and T2w: 0.34-0.5 × 0.34-0.5 × 3-5 mm) and Hyperfine 64mT (FLAIR: 1.6 × 1.6 × 5 mm, T1w: 1.5 × 1.5 × 5 mm, and T2w: 1.5 × 1.5 × 5 mm) scanners at two centers. Images were reviewed by neuroradiologists. MS lesions were measured manually and segmented using an automated algorithm. Statistical analyses assessed accuracy and variability of segmentations across scanners and systematic scanner biases in automated volumetric measurements. Lesions were identified on 64mT scans in 94% (31/33) of patients with confirmed MS. The average smallest lesions manually detected were 5.7 ± 1.3 mm in maximum diameter at 64mT vs 2.1 ± 0.6 mm at 3T, approaching the spatial resolution of the respective scanner sequences (3T: 1 mm, 64mT: 5 mm slice thickness). Automated lesion volume estimates were highly correlated between 3T and 64mT scans (r = 0.89, p 1.0 ml and all lesions > 1.5 ml being detected. Using multi-acquisition volume averaging, we were able to generate 1.6 mm isotropic images on the 64mT device. Overall, our results demonstrate that in established MS, a portable 64mT MRI scanner can identify white matter lesions, and that automated estimates of total lesion volume correlate with measurements from 3T scans.

YNICL Journal 2021 Journal Article

Cervical and thoracic cord atrophy in multiple sclerosis phenotypes: Quantification and correlation with clinical disability

• Yair Mina
• Shila Azodi
• Tsemacha Dubuche
• Frances Andrada
• Ikesinachi Osuorah
• Joan Ohayon
• Irene Cortese
• Tianxia Wu

OBJECTIVE: We sought to characterize spinal cord atrophy along the entire spinal cord in the major multiple sclerosis (MS) phenotypes, and evaluate its correlation with clinical disability. METHODS: -weighted images were automatically reformatted at each point along the cord. Spinal cord cross-sectional area (SCCSA) were calculated from C1-T10 vertebral body levels and profile plots were compared across phenotypes. Average values from C2-3, C4-5, and T4-9 regions were compared across phenotypes and correlated with clinical scores, and then categorized as atrophic/normal based on z-scores derived from controls, to compare clinical scores between subgroups. In a subset of relapsing-remitting cases with longitudinal scans these regions were compared to change in clinical scores. RESULTS: The cross-sectional study consisted of 149 adults diagnosed with relapsing-remitting MS (RRMS), 49 with secondary-progressive MS (SPMS), 58 with primary-progressive MS (PPMS) and 48 controls. The longitudinal study included 78 RRMS cases. Compared to controls, all MS groups had smaller average regions except RRMS in T4-9 region. In all MS groups, SCCSA from all regions, particularly the cervical cord, correlated with most clinical measures. In the RRMS cohort, 22% of cases had at least one atrophic region, whereas in progressive MS the rate was almost 70%. Longitudinal analysis showed correlation between clinical disability and cervical cord thinning. CONCLUSIONS: Spinal cord atrophy was prevalent across MS phenotypes, with regional measures from the RRMS cohort and the progressive cohort, including SPMS and PPMS, being correlated with disability. Longitudinal changes in the spinal cord were documented in RRMS cases, making it a potential marker for disease progression. While cervical SCCSA correlated with most disability and progression measures, inclusion of thoracic measurements improved this correlation and allowed for better subgrouping of spinal cord phenotypes. Cord atrophy is an important and easily obtainable imaging marker of clinical and sub-clinical progression in all MS phenotypes, and such measures can play a key role in patient selection for clinical trials.

YNICL Journal 2021 Journal Article

Fully automated detection of paramagnetic rims in multiple sclerosis lesions on 3T susceptibility-based MR imaging

• Carolyn Lou
• Pascal Sati
• Martina Absinta
• Kelly Clark
• Jordan D. Dworkin
• Alessandra M. Valcarcel
• Matthew K. Schindler
• Daniel S. Reich

BACKGROUND AND PURPOSE: The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis lesion. Increased prevalence of these paramagnetic rim lesions is associated with a more severe disease course in MS, but manual identification is time-consuming. We present APRL, a method to automatically detect paramagnetic rim lesions on 3T T2*-phase images. METHODS: T1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 20 subjects with MS. The images were then processed with automated lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 951 lesions were identified, 113 (12%) of which contained a paramagnetic rim. We divided our data into a training set (16 patients, 753 lesions) and a testing set (4 patients, 198 lesions), fit a random forest classification model on the training set, and assessed our ability to classify paramagnetic rim lesions on the test set. RESULTS: The number of paramagnetic rim lesions per subject identified via our automated lesion labelling method was highly correlated with the gold standard count per subject, r = 0.86 (95% CI [0.68, 0.94]). The classification algorithm using radiomic features classified lesions with an area under the curve of 0.82 (95% CI [0.74, 0.92]). CONCLUSION: This study develops a fully automated technique, APRL, for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.

YNICL Journal 2020 Journal Article

Magnetic resonance imaging in multiple sclerosis animal models: A systematic review, meta-analysis, and white paper

• Benjamin V. Ineichen
• Pascal Sati
• Tobias Granberg
• Martina Absinta
• Nathanael J. Lee
• Jennifer A. Lefeuvre
• Daniel S. Reich

Magnetic resonance imaging (MRI) is the most important paraclinical tool for assessing drug response in multiple sclerosis (MS) clinical trials. As such, MRI has also been widely used in preclinical research to investigate drug efficacy and pathogenic aspects in MS animal models. Keeping track of all published preclinical imaging studies, and possible new therapeutic approaches, has become difficult considering the abundance of studies. Moreover, comparisons between studies are hampered by methodological differences, especially since small differences in an MRI protocol can lead to large differences in tissue contrast. We therefore provide a comprehensive qualitative overview of preclinical MRI studies in the field of neuroinflammatory and demyelinating diseases, aiming to summarize experimental setup, MRI methodology, and risk of bias. We also provide estimates of the effects of tested therapeutic interventions by a meta-analysis. Finally, to improve the standardization of preclinical experiments, we propose guidelines on technical aspects of MRI and reporting that can serve as a framework for future preclinical studies using MRI in MS animal models. By implementing these guidelines, clinical translation of findings will be facilitated, and could possibly reduce experimental animal numbers.

YNICL Journal 2020 Journal Article

Progressive multifocal leukoencephalopathy lesion and brain parenchymal segmentation from MRI using serial deep convolutional neural networks

• Omar Al-Louzi
• Snehashis Roy
• Ikesinachi Osuorah
• Prasanna Parvathaneni
• Bryan R. Smith
• Joan Ohayon
• Pascal Sati
• Dzung L. Pham

Progressive multifocal leukoencephalopathy (PML) is a rare opportunistic brain infection caused by the JC virus and associated with substantial morbidity and mortality. Accurate MRI assessment of PML lesion burden and brain parenchymal atrophy is of decisive value in monitoring the disease course and response to therapy. However, there are currently no validated automatic methods for quantification of PML lesion burden or associated parenchymal volume loss. Furthermore, manual brain or lesion delineations can be tedious, require the use of valuable time resources by radiologists or trained experts, and are often subjective. In this work, we introduce JCnet (named after the causative viral agent), an end-to-end, fully automated method for brain parenchymal and lesion segmentation in PML using consecutive 3D patch-based convolutional neural networks. The network architecture consists of multi-view feature pyramid networks with hierarchical residual learning blocks containing embedded batch normalization and nonlinear activation functions. The feature maps across the bottom-up and top-down pathways of the feature pyramids are merged, and an output probability membership generated through convolutional pathways, thus rendering the method fully convolutional. Our results show that this approach outperforms and improves longitudinal consistency compared to conventional, state-of-the-art methods of healthy brain and multiple sclerosis lesion segmentation, utilized here as comparators given the lack of available methods validated for use in PML. The ability to produce robust and accurate automated measures of brain atrophy and lesion segmentation in PML is not only valuable clinically but holds promise toward including standardized quantitative MRI measures in clinical trials of targeted therapies. Code is available at: https://github.com/omarallouz/JCnet.

YNICL Journal 2020 Journal Article

RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis

• Germán Barquero
• Francesco La Rosa
• Hamza Kebiri
• Po-Jui Lu
• Reza Rahmanzadeh
• Matthias Weigel
• Mário João Fartaria
• Tobias Kober

OBJECTIVES: In multiple sclerosis (MS), the presence of a paramagnetic rim at the edge of non-gadolinium-enhancing lesions indicates perilesional chronic inflammation. Patients featuring a higher paramagnetic rim lesion burden tend to have more aggressive disease. The objective of this study was to develop and evaluate a convolutional neural network (CNN) architecture (RimNet) for automated detection of paramagnetic rim lesions in MS employing multiple magnetic resonance (MR) imaging contrasts. MATERIALS AND METHODS: Imaging data were acquired at 3 Tesla on three different scanners from two different centers, totaling 124 MS patients, and studied retrospectively. Paramagnetic rim lesion detection was independently assessed by two expert raters on T2*-phase images, yielding 462 rim-positive (rim+) and 4857 rim-negative (rim-) lesions. RimNet was designed using 3D patches centered on candidate lesions in 3D-EPI phase and 3D FLAIR as input to two network branches. The interconnection of branches at both the first network blocks and the last fully connected layers favors the extraction of low and high-level multimodal features, respectively. RimNet's performance was quantitatively evaluated against experts' evaluation from both lesion-wise and patient-wise perspectives. For the latter, patients were categorized based on a clinically relevant threshold of 4 rim+ lesions per patient. The individual prediction capabilities of the images were also explored and compared (DeLong test) by testing a CNN trained with one image as input (unimodal). RESULTS: The unimodal exploration showed the superior performance of 3D-EPI phase and 3D-EPI magnitude images in the rim+/- classification task (AUC = 0.913 and 0.901), compared to the 3D FLAIR (AUC = 0.855, Ps < 0.0001). The proposed multimodal RimNet prototype clearly outperformed the best unimodal approach (AUC = 0.943, P < 0.0001). The sensitivity and specificity achieved by RimNet (70.6% and 94.9%, respectively) are comparable to those of experts at the lesion level. In the patient-wise analysis, RimNet performed with an accuracy of 89.5% and a Dice coefficient (or F1 score) of 83.5%. CONCLUSIONS: The proposed prototype showed promising performance, supporting the usage of RimNet for speeding up and standardizing the paramagnetic rim lesions analysis in MS.

YNIMG Journal 2019 Journal Article

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

• Charley Gros
• Benjamin De Leener
• Atef Badji
• Josefina Maranzano
• Dominique Eden
• Sara M. Dupont
• Jason Talbott
• Ren Zhuoquiong

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.

YNIMG Journal 2018 Journal Article

Impulse response timing differences in BOLD and CBV weighted fMRI

• Jacco A. de Zwart
• Peter van Gelderen
• Matthew K. Schindler
• Pascal Sati
• Jiaen Liu
• Daniel S. Reich
• Jeff H. Duyn

Recent advances in BOLD fMRI scan techniques have substantially improved spatial and temporal resolution, currently reaching to sub-millimeter and sub-second levels respectively. Unfortunately, there remain physiological barriers that prevent achieving this resolution in practice. BOLD contrast relies on the hemodynamic response to neuronal activity, whose associated cerebral blood oxygenation (CBO) changes may spread over several millimeters and last several seconds. Recent reports have suggested that significant improvements may be possible with cerebral blood volume (CBV)-weighted fMRI, which highlights the CBV changes rather than the BOLD changes associated with the hemodynamic response. Nevertheless, quantitative comparisons between CBV and BOLD are sparse, in particular regarding their temporal characteristics in human brain. To address this, we studied a cohort of subjects that received injection of ferumoxytol, an intravascular iron-oxide based contrast agent that introduces strong CBV contrast. An event-related visual stimulus paradigm was used to compare the impulse response (IR) for CBV and BOLD contrast, obtained with and without ferumoxytol, respectively. Experiments performed at 7 T (n = 5) at 1. 2–1. 5 mm spatial and 1 s temporal resolution showed that the onset time and time-to-peak of the CBV IR averaged 0. 8 and 3. 5 s respectively, both 0. 6 s shorter than the BOLD IR. While significant, these improvements are relatively small and not expected to lead to practical advantages for the extraction of temporal information about neural activity. Nonlinearities in the observed IR were also compared and found to be similar between the CBV and BOLD, indicating that these are likely not caused by a ‘ceiling’ effect in the CBO response, but rather support a previously proposed model of vascular compliance, in which changes in vascular tone elicited by a preceding stimulus affect the IR.

YNICL Journal 2017 Journal Article

Longitudinal MR spectroscopy of neurodegeneration in multiple sclerosis with diffusion of the intra-axonal constituent N-acetylaspartate

• Emily Turner Wood
• Ece Ercan
• Pascal Sati
• Irene C.M. Cortese
• Itamar Ronen
• Daniel S. Reich

Multiple sclerosis (MS) is a pathologically complex CNS disease: inflammation, demyelination, and neuroaxonal degeneration occur concurrently and may depend on one another. Current therapies are aimed at the immune-mediated, inflammatory destruction of myelin, whereas axonal degeneration is ongoing and not specifically targeted. Diffusion-weighted magnetic resonance spectroscopy can measure the diffusivity of metabolites in vivo, such as the axonal/neuronal constituent N-acetylaspartate, allowing compartment-specific assessment of disease-related changes. Previously, we found significantly lower N-acetylaspartate diffusivity in people with MS compared to healthy controls (Wood et al. , 2012) suggesting that this technique can measure axonal degeneration and could be useful in developing neuroprotective agents. In this longitudinal study, we found that N-acetylaspartate diffusivity decreased by 8. 3% (p<0. 05) over 6months in participants who were experiencing clinical or MRI evidence of inflammatory activity (n=13), whereas there was no significant change in N-acetylaspartate diffusivity in the context of clinical and radiological stability (n=6). As N-acetylaspartate diffusivity measurements are thought to more specifically reflect the intra-axonal space, these data suggest that N-acetylaspartate diffusivity can report on axonal health on the background of multiple pathological processes in MS, both cross-sectionally and longitudinally.

YNIMG Journal 2017 Journal Article

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

• Aaron Carass
• Snehashis Roy
• Amod Jog
• Jennifer L. Cuzzocreo
• Elizabeth Magrath
• Adrian Gherman
• Julia Button
• James Nguyen

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4. 4 time-points, and test data of fourteen subjects with a mean of 4. 4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website 2 2 The Challenge Evaluation Website is: http: //smart-stats-tools. org/lesion-challenge-2015 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

YNICL Journal 2016 Journal Article

PREVAIL: Predicting Recovery through Estimation and Visualization of Active and Incident Lesions

• Jordan D. Dworkin
• Elizabeth M. Sweeney
• Matthew K. Schindler
• Salim Chahin
• Daniel S. Reich
• Russell T. Shinohara

OBJECTIVE: The goal of this study was to develop a model that integrates imaging and clinical information observed at lesion incidence for predicting the recovery of white matter lesions in multiple sclerosis (MS) patients. METHODS: Demographic, clinical, and magnetic resonance imaging (MRI) data were obtained from 60 subjects with MS as part of a natural history study at the National Institute of Neurological Disorders and Stroke. A total of 401 lesions met the inclusion criteria and were used in the study. Imaging features were extracted from the intensity-normalized T1-weighted (T1w) and T2-weighted sequences as well as magnetization transfer ratio (MTR) sequence acquired at lesion incidence. T1w and MTR signatures were also extracted from images acquired one-year post-incidence. Imaging features were integrated with clinical and demographic data observed at lesion incidence to create statistical prediction models for long-term damage within the lesion. VALIDATION: The performance of the T1w and MTR predictions was assessed in two ways: first, the predictive accuracy was measured quantitatively using leave-one-lesion-out cross-validated (CV) mean-squared predictive error. Then, to assess the prediction performance from the perspective of expert clinicians, three board-certified MS clinicians were asked to individually score how similar the CV model-predicted one-year appearance was to the true one-year appearance for a random sample of 100 lesions. RESULTS: The cross-validated root-mean-square predictive error was 0.95 for normalized T1w and 0.064 for MTR, compared to the estimated measurement errors of 0.48 and 0.078 respectively. The three expert raters agreed that T1w and MTR predictions closely resembled the true one-year follow-up appearance of the lesions in both degree and pattern of recovery within lesions. CONCLUSION: This study demonstrates that by using only information from a single visit at incidence, we can predict how a new lesion will recover using relatively simple statistical techniques. The potential to visualize the likely course of recovery has implications for clinical decision-making, as well as trial enrichment.

YNICL Journal 2016 Journal Article

Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions

• Elizabeth M. Sweeney
• Russell T. Shinohara
• Blake E. Dewey
• Matthew K. Schindler
• John Muschelli
• Daniel S. Reich
• Ciprian M. Crainiceanu
• Ani Eloyan

The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair - all of which are visible on structural magnetic resonance imaging (MRI) and potentially modifiable by pharmacological therapy. In this paper, we introduce two statistical models for relating voxel-level, longitudinal, multi-sequence structural MRI intensities within MS lesions to clinical information and therapeutic interventions: (1) a principal component analysis (PCA) and regression model and (2) function-on-scalar regression models. To do so, we first characterize the post-lesion incidence repair process on longitudinal, multi-sequence structural MRI from 34 MS patients as voxel-level intensity profiles. For the PCA regression model, we perform PCA on the intensity profiles to develop a voxel-level biomarker for identifying slow and persistent, long-term intensity changes within lesion tissue voxels. The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist). On a scale of 1 to 4, with 4 being the highest quality, the neuroradiologist gave the score on the first PC a median quality rating of 4 (95% CI: [4,4]), and the neurologist gave the score a median rating of 3 (95% CI: [3,3]). We then relate the biomarker to the clinical information in a mixed model framework. Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue. The function-on-scalar regression model allows for assessment of the post-incidence time points at which the covariates are associated with the profiles. In the function-on-scalar regression, both age and distance to the boundary were found to have a statistically significant association with the lesion intensities at some time point. The two models presented in this article show promise for understanding the mechanisms of tissue damage in MS and for evaluating the impact of treatments for the disease in clinical trials.

YNIMG Journal 2016 Journal Article

Statistical estimation of T1 relaxation times using conventional magnetic resonance imaging

• Amanda F. Mejia
• Elizabeth M. Sweeney
• Blake Dewey
• Govind Nair
• Pascal Sati
• Colin Shea
• Daniel S. Reich
• Russell T. Shinohara

Quantitative T 1 maps estimate T 1 relaxation times and can be used to assess diffuse tissue abnormalities within normal-appearing tissue. T 1 maps are popular for studying the progression and treatment of multiple sclerosis (MS). However, their inclusion in standard imaging protocols remains limited due to the additional scanning time and expert calibration required and susceptibility to bias and noise. Here, we propose a new method of estimating T 1 maps using four conventional MR images, which are intensity-normalized using cerebellar gray matter as a reference tissue and related to T 1 using a smooth regression model. Using cross-validation, we generate statistical T 1 maps for 61 subjects with MS. The statistical maps are less noisy than the acquired maps and show similar reproducibility. Tests of group differences in normal-appearing white matter across MS subtypes give similar results using both methods.

YNICL Journal 2015 Journal Article

Corrigendum to “Statistical normalization techniques for magnetic resonance imaging” [NeuroImage: Clinical 6 (2014) 9–19]

• Russell T. Shinohara
• Elizabeth M. Sweeney
• Jeff Goldsmith
• Navid Shiee
• Farrah J. Mateen
• Peter A. Calabresi
• Samson Jarso
• Dzung L. Pham

YNICL Journal 2015 Journal Article

Detection of demyelination in multiple sclerosis by analysis of T 2 * relaxation at 7 T

• Xiaozhen Li
• Peter van Gelderen
• Pascal Sati
• Jacco A. de Zwart
• Daniel S. Reich
• Jeff H. Duyn

Multiple sclerosis (MS) is a relatively common cause of inflammatory demyelinating lesions of the central nervous system. In an attempt to detect and characterize ongoing demyelination in MS patient brains, we used a novel magnetic resonance imaging (MRI) technique, involving the fitting of a three-component model to the T 2 * relaxation behavior at high-field (7 T). This model allowed estimation of the amount of myelin water (and thus indirectly myelin content), axonal water, and interstitial water. In this study, 25 relapsing–remitting MS patients underwent a 7 T MRI from which 12 gadolinium-enhancing lesions, 61 non-enhancing lesions, and their corresponding contralateral normal appearing white matter (NAWM) regions were analyzed. In both enhancing and non-enhancing lesions, the amplitude of myelin water was significantly decreased, and interstitial and axonal water were increased relative to the contralateral NAWM. Longer relaxation time T 2 * of interstitial and axonal water, and lower frequency shift of axonal water, were also observed in both enhancing and non-enhancing lesions when compared to the contralateral NAWM. No significant difference was found between enhancing lesions and non-enhancing lesions. These findings suggest that the fitting of a three-component model to the T 2 * decay curve in MS lesions may help to quantify myelin loss.

JBHI Journal 2015 Journal Article

Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation

• Snehashis Roy
• Qing He
• Elizabeth Sweeney
• Aaron Carass
• Daniel S. Reich
• Jerry L. Prince
• Dzung L. Pham

Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

YNICL Journal 2014 Journal Article

Statistical normalization techniques for magnetic resonance imaging

• Russell T. Shinohara
• Elizabeth M. Sweeney
• Jeff Goldsmith
• Navid Shiee
• Farrah J. Mateen
• Peter A. Calabresi
• Samson Jarso
• Dzung L. Pham

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

YNIMG Journal 2014 Journal Article

Sub-millimeter imaging of brain-free water for rapid volume assessment in atrophic brains

• Katherine C. Gao
• Govind Nair
• Irene C.M. Cortese
• Alan Koretsky
• Daniel S. Reich

Introduction Cerebral atrophy occurs in healthy aging, and in disease processes such as multiple sclerosis (MS), it correlates with disability accumulation. Imaging measurements of brain atrophy are commonly based on tissue segmentation, which is susceptible to classification errors and inconsistencies. High-resolution imaging techniques with strong contrast between brain parenchyma and cerebrospinal fluid (CSF) might allow fully automated, rapid, threshold-based determination of the free water in the brain. We hypothesized that total brain-free-water (BFW) volume and BFW volume expressed as a normalized fraction of the intracranial volume (“BFW fraction”), determined from heavily T2-weighted images, would be useful surrogates for cerebral atrophy and therefore would correlate with clinical measures of disability in MS. Methods Whole brains of 83 MS cases and 7 healthy volunteers were imaged with a 4. 7-min, heavily T2-weighted sequence on a 3T MRI scanner, acquiring 650-μm isotropic voxels. MS cases were clinically assessed on the Expanded Disability Status Scale (EDSS), Scripps Neurological Rating Scale (SNRS), Paced Auditory Serial Addition Test (PASAT), 9-Hole Peg Test (9HPT), Symbol Digit Modalities Test (SDMT), and 25-Foot Timed Walk. Twelve of the MS cases were rescanned within an average of 1. 8months to assess reproducibility. Automated calculations of BFW volume and BFW fraction were correlated with clinical measures of disability upon adjusting for age and sex. Results were compared to data from T1-based approaches (SIENAX and Lesion-TOADS). Results and discussion BFW volume was automatically derived from heavily T2-weighted images with no need for separate skull stripping. BFW volume and fraction had mean scan–rescan coefficients of variation of 1. 5% and 1. 9%, respectively, similar to the T1-based approaches tested here. BFW fraction more strongly correlated with clinical measures than T1-derived results. Among those clinical measures, modality-specific disability scores, such as SDMT and 9HPT, were more strongly associated with BFW fraction than composite measures, such as EDSS and SNRS. Conclusion The BFW method robustly estimates cerebral atrophy in an automated, fast, and reliable manner, and as such may prove a useful addition to imaging protocols for clinical practice and trials.

YNIMG Journal 2013 Journal Article

Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view

• Min Chen
• Aaron Carass
• Jiwon Oh
• Govind Nair
• Dzung L. Pham
• Daniel S. Reich
• Jerry L. Prince

Spinal cord segmentation is an important step in the analysis of neurological diseases such as multiple sclerosis. Several studies have shown correlations between disease progression and metrics relating to spinal cord atrophy and shape changes. Current practices primarily involve segmenting the spinal cord manually or semi-automatically, which can be inconsistent and time-consuming for large datasets. An automatic method that segments the spinal cord and cerebrospinal fluid from magnetic resonance images is presented. The method uses a deformable atlas and topology constraints to produce results that are robust to noise and artifacts. The method is designed to be easily extended to new data with different modalities, resolutions, and fields of view. Validation was performed on two distinct datasets. The first consists of magnetization transfer-prepared T2*-weighted gradient-echo MRI centered only on the cervical vertebrae (C1–C5). The second consists of T1-weighted MRI that covers both the cervical and portions of the thoracic vertebrae (C1–T4). Results were found to be highly accurate in comparison to manual segmentations. A pilot study was carried out to demonstrate the potential utility of this new method for research and clinical studies of multiple sclerosis.

YNIMG Journal 2013 Journal Article

Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes

• Lei Huang
• Jeff Goldsmith
• Philip T. Reiss
• Daniel S. Reich
• Ciprian M. Crainiceanu

Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian “scalar-on-image” regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients.

YNIMG Journal 2013 Journal Article

Micro-compartment specific T2⁎ relaxation in the brain

• Pascal Sati
• Peter van Gelderen
• Afonso C. Silva
• Daniel S. Reich
• Hellmut Merkle
• Jacco A. de Zwart
• Jeff H. Duyn

MRI at high field can be sensitized to the magnetic properties of tissues, which introduces a signal dependence on the orientation of white matter (WM) fiber bundles relative to the magnetic field. In addition, study of the NMR relaxation properties of this signal has indicated contributions from compartmentalized water environments inside and outside the myelin sheath that may be separable. Here we further investigated the effects of water compartmentalization on the MRI signal with the goal of extracting compartment-specific information. By comparing MRI measurements of human and marmoset brain at 7T with magnetic field modeling, we show that: (1) water between the myelin lipid bilayers, in the axonal, and in the interstitial space each experience characteristic magnetic field effects that depend on fiber orientation (2) these field effects result in characteristic relaxation properties and frequency shifts for these compartments; and (3) compartmental contributions may be separated by multi-component fitting of the MRI signal relaxation (i. e. decay) curve. We further show the potential application of these findings to the direct mapping of myelin content and assessment of WM fiber integrity with high field MRI.

YNICL Journal 2013 Journal Article

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI

• Elizabeth M. Sweeney
• Russell T. Shinohara
• Navid Shiee
• Farrah J. Mateen
• Avni A. Chudgar
• Jennifer L. Cuzzocreo
• Peter A. Calabresi
• Dzung L. Pham

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.

YNICL Journal 2013 Journal Article

Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling

• Blake C. Jones
• Govind Nair
• Colin D. Shea
• Ciprian M. Crainiceanu
• Irene C.M. Cortese
• Daniel S. Reich

Brain atrophy, measured by MRI, has been proposed as a useful surrogate marker for disease progression in multiple sclerosis (MS). However, it is conventionally assumed that the accurate quantification of brain atrophy is made difficult, if not impossible, by changes in the parameters of the MRI acquisition, which are almost inevitable over the course of a longitudinal study since MRI technology changes rapidly. This state of affairs can negatively affect clinical trial design and limit the use of historical data. Here, we investigate whether we can coherently estimate brain atrophy rates in a heterogeneous MS sample via linear mixed-effects multivariable regression, incorporating three critical assumptions: (1) using age at time of scanning, rather than time since baseline, as the regressor of interest; (2) scanning individuals with a variety of techniques; and (3) introducing a simple additive correction for major differences in MRI protocol. We fit the model to several measures of brain volume as the outcome in two MS populations: 1123 scans from 195 cases acquired for over approximately 7 years in two natural history protocols (Cohort 1), and 1331 scans from 69 cases seen for over 11 years who were primarily treated with two specific MS disease-modifying therapies (Cohort 2). We compared the mixed-effects model with additive correction for MRI acquisition parameters to a model fit without this correction and performed sample-size calculations to provide an estimate of the number of participants in an MS clinical trial that might be required to see a therapeutic effect of treatment using the approach described here. The results show that without the additive correction for T1-weighted protocol parameters, atrophy was underestimated and subject-specific estimates were more narrowly distributed about the population mean. Ventricular CSF is the most consistently estimated brain volume, with a mean of 2.8%/year increase in Cohort 1 and 4.4%/year increase in Cohort 2. An interesting observation was that gray matter volume decreased and white matter volume remained essentially unchanged in both cohorts, suggesting that changes in ventricular CSF volume are a surrogate for changes in gray matter volume. In conclusion, the mixed-effects modeling framework presented here allows effective use of heterogeneously acquired and historical data in the study of brain atrophy in MS, potentially simplifying the design of future single- and multi-site clinical trials and natural history studies.

YNIMG Journal 2012 Journal Article

In vivo quantification of T2⁎ anisotropy in white matter fibers in marmoset monkeys

• Pascal Sati
• Afonso C. Silva
• Peter van Gelderen
• Maria I. Gaitan
• Jillian E. Wohler
• Steven Jacobson
• Jeff H. Duyn
• Daniel S. Reich

T2 ⁎-weighted MRI at high field is a promising approach for studying noninvasively the tissue structure and composition of the brain. However, the biophysical origin of T2 ⁎ contrast, especially in white matter, remains poorly understood. Recent work has shown that R2 ⁎ (=1/T2 ⁎) may depend on the tissue's orientation relative to the static magnetic field (B0) and suggested that this dependence could be attributed to local anisotropy in the magnetic properties of brain tissue. In the present work, we analyzed high-resolution, multi-gradient-echo images of in vivo marmoset brains at 7T, and compared them with ex vivo diffusion tensor images, to show that R2 ⁎ relaxation in white matter is highly sensitive to the fiber orientation relative to the main field. We directly demonstrate this orientation dependence by performing in vivo multi-gradient-echo experiments in two orthogonal brain positions, uncovering a nearly 50% change in the R2 ⁎ relaxation rate constant of the optic radiations. We attribute this substantial R2 ⁎ anisotropy to local subvoxel susceptibility effects arising from the highly ordered and anisotropic structure of the myelin sheath.

YNIMG Journal 2011 Journal Article

Direct segmentation of the major white matter tracts in diffusion tensor images

• Pierre-Louis Bazin
• Chuyang Ye
• John A. Bogovic
• Navid Shiee
• Daniel S. Reich
• Jerry L. Prince
• Dzung L. Pham

Diffusion-weighted images of the human brain are acquired more and more routinely in clinical research settings, yet segmenting and labeling white matter tracts in these images is still challenging. We present in this paper a fully automated method to extract many anatomical tracts at once on diffusion tensor images, based on a Markov random field model and anatomical priors. The approach provides a direct voxel labeling, models explicitly fiber crossings and can handle white matter lesions. Experiments on simulations and repeatability studies show robustness to noise and reproducibility of the algorithm, which has been made publicly available.

YNIMG Journal 2011 Journal Article

Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis

• Jeff Goldsmith
• Ciprian M. Crainiceanu
• Brian S. Caffo
• Daniel S. Reich

Diffusion tensor imaging (DTI) enables noninvasive parcellation of cerebral white matter into its component fiber bundles or tracts. These tracts often subserve specific functions, and damage to the tracts can therefore result in characteristic forms of disability. Attempts to quantify the extent of tract-specific damage have been limited in part by substantial spatial variation of imaging properties from one end of a tract to the other, variation that can be compounded by the effects of disease. Here, we develop a “penalized functional regression” procedure to analyze spatially normalized tract profiles, which powerfully characterize such spatial variation. The central idea is to identify and emphasize portions of a tract that are more relevant to a clinical outcome score, such as case status or degree of disability. The procedure also yields a “tract abnormality score” for each tract and MRI index studied. Importantly, the weighting function used in this procedure is constrained to be smooth, and the statistical associations are estimated using generalized linear models. We test the method on data from a cross-sectional MRI and functional study of 115 multiple-sclerosis cases and 42 healthy volunteers, considering a range of quantitative MRI indices, white-matter tracts, and clinical outcome scores, and using training and testing sets to validate the results. We show that attention to spatial variation yields up to 15% (mean across all tracts and MRI indices: 6. 4%) improvement in the ability to discriminate multiple sclerosis cases from healthy volunteers. Our results confirm that comprehensive analysis of white-matter tract-specific imaging data improves with knowledge and characterization of the normal spatial variation.

YNIMG Journal 2011 Journal Article

Population-wide principal component-based quantification of blood–brain-barrier dynamics in multiple sclerosis

• Russell T. Shinohara
• Ciprian M. Crainiceanu
• Brian S. Caffo
• María Inés Gaitán
• Daniel S. Reich

The processes by which new white matter lesions in multiple sclerosis (MS) develop are only partially understood. Much of this understanding has come through magnetic resonance imaging (MRI) of the human brain. One of the hallmarks of new lesion development in MS is enhancement on T1-weighted MRI scans following the intravenous administration of a gadolinium-based contrast agent that shortens the longitudinal relaxation time of the tissue. Visible enhancement on the MRI results from the opening of the blood–brain barrier and reveals areas of active inflammation. The incidence and number of existing enhancing lesions are common outcome measures used in MS treatment clinical trials. Dynamic-contrast-enhanced MRI (DCE-MRI) can estimate the rate at which contrast agents pass from the plasma to MS lesions. In this paper, we develop a principal component-based framework for the analysis of these data that provides biologically meaningful quantification of blood–brain–barrier opening in new MS lesions. To accomplish this, we use functional principal components analysis to study directions of variation in the voxel-level time series of intensities both within and across subjects. The analysis reveals and allows quantification of typical spatiotemporal enhancement patterns in acute MS lesions, providing measures of magnitude, rate, shape (ring-like vs. nodular), and dynamics (centrifugal vs. centripetal). Across 10 subjects with relapsing–remitting and primary progressive MS, we found subjects to have between 0 and 12 gadolinium-enhancing lesions, the majority of which enhanced centripetally. We quantified the spatiotemporal behavior within each of these lesions using novel measures. Further application of these techniques will determine the extent to which these lesion measures can predict or track response to therapy or long-term prognosis in this disorder.

YNIMG Journal 2010 Journal Article

A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions

• Navid Shiee
• Pierre-Louis Bazin
• Arzu Ozturk
• Daniel S. Reich
• Peter A. Calabresi
• Dzung L. Pham

We describe a new fully automatic method for the segmentation of brain images that contain multiple sclerosis white matter lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas-based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing such as cortical unfolding or diffeomorphic shape analysis techniques. Evaluation with both simulated and real data sets demonstrates that the method has an accuracy competitive with state-of-the-art MS lesion segmentation methods, while simultaneously segmenting the whole brain.

YNIMG Journal 2010 Journal Article

Automated vs. conventional tractography in multiple sclerosis: Variability and correlation with disability

• Daniel S. Reich
• Arzu Ozturk
• Peter A. Calabresi
• Susumu Mori

Diffusion-tensor-imaging fiber tractography enables interrogation of brain white matter tracts that subserve different functions. However, tract reconstruction can be labor and time intensive and can yield variable results that may reduce the power to link imaging abnormalities with disability. Automated segmentation of these tracts would help make tract-specific imaging clinically useful, but implementation of such segmentation is problematic in the presence of diseases that alter brain structure. In this work, we investigated an automated tract-probability-mapping scheme and applied it to multiple sclerosis, comparing the results to those derived from conventional tractography. We found that the automated method has consistently lower scan-rescan variability (typically 0. 7–1. 5% vs. up to 3% for conventional tractography) and avoids problems related to tractography failures within and around lesions. In the corpus callosum, optic radiation, and corticospinal tract, tract-specific MRI indices calculated by the two methods were moderately to strongly correlated, though systematic, tract-specific differences were present. In these tracts, the two methods also yielded similar correlation coefficients relating tract-specific MRI indices to clinical disability scores. In the optic tract, the automated method failed. With judicious application, therefore, the automated method may be useful for studies that investigate the relationship between imaging findings and clinical outcomes in disease.

YNIMG Journal 2008 Journal Article

Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

• Kegang Hua
• Jiangyang Zhang
• Setsu Wakana
• Hangyi Jiang
• Xin Li
• Daniel S. Reich
• Peter A. Calabresi
• James J. Pekar

Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.

YNIMG Journal 2007 Journal Article

Multiparametric magnetic resonance imaging analysis of the corticospinal tract in multiple sclerosis☆

• Daniel S. Reich
• Seth A. Smith
• Kathleen M. Zackowski
• Eliza M. Gordon-Lipkin
• Craig K. Jones
• Jonathan A.D. Farrell
• Susumu Mori
• Peter C.M. van Zijl

Background/purpose Muscle weakness is an important feature of multiple sclerosis and is responsible for much of the disability associated with that condition. Here, we describe the quantitative magnetic resonance imaging (MRI) attributes of the major intracerebral motor pathway – the corticospinal tract – in multiple sclerosis. To do so, we develop an intuitive method for creating and displaying spatially normalized tract-specific imaging data. Methods In 75 individuals with multiple sclerosis and 29 healthy controls, the corticospinal tracts were reconstructed from diffusion tensor imaging at 3 T. Multiple MRI indices – T2 relaxation time; fractional anisotropy; mean, longitudinal, and transverse diffusivity; and magnetization transfer ratio – were examined within the reconstructed tracts. Spatially normalized tract profiles were created to compare, across subjects, the variation in MRI index as a function of tract position. Results Each index’s tract profile had a characteristic shape. Individual subjects had markedly abnormal tract profiles, particularly at lesion sites. On average, tract profiles were different between patients and controls, particularly in the subcortical white matter and corona radiata, for all indices examined except for fractional anisotropy. Magnetization transfer ratio was further decreased in subjects with secondary progressive disease. Tract asymmetry was increased in multiple sclerosis compared to controls. Conclusion Multiparametric MRI allows rapid detection, localization, and characterization of tract-specific abnormalities in multiple sclerosis. Tract profiles bridge the gap between whole-brain imaging of neurological disease and the interrogation of individual, functionally relevant subsystems.