Multiple sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness

Objective: To classify spinal cord lesions in multiple sclerosis (MS) based on white matter (WM) columns, and grey matter (GM) and WM involvement using magnetic resonance imaging (MRI). Background The localization of spinal cord lesions has not been well documented on MRI, although morphology and distribution of plaques have been characterized in post-mortem studies. Design/Methods: MRI was performed on the cervical cord in 16 MS patients (10 female, mean age 44.8) (8 relapsing remitting (RR) MS, 7 secondary progressive (SP) MS, 1 clinically isolated syndrome (CIS)). Axial phase sensitive inversion recovery (PSIR) and gradient-echo sequences were acquired. Two readers reviewed both images simultaneously and reached a consensus about classification of lesions by location: anterior (AC), lateral (LC) or posterior columns (PC). A sub-classification of lesions in mixed WM/GM and those involving WM only was also performed. Lesions were outlined manually on PSIR images and compared with mean cord area. Results: 51 lesions were identified, 24 in RRMS, 22 in SPMS, 5 in CIS. Three lesions were seen in AC (5.8%), 18 in PC (35.3%) and 30 in LC (58.9%). 33% of lesions involved WM alone and 67% involved both GM and WM; no pure GM lesions were identified. Mean lesion area in AC was 4.3mm2, LC 8.5mm2, PC 11.3mm2, with lesions in each column covering 6.1%, 12% and 16.1% of mean cord area respectively, mean lesion length in AC 18.3mm, LC 17.6mm and PC 24.8mm. Conclusions: Most lesions seen involved GM and WM, and were in the PC and LC, in agreement with post-mortem studies. However, no pure GM lesions were seen, suggesting technical limitations or a difference in patient characteristics from post-mortem studies. MRI may permit classification of spinal cord lesion location, area and length, which maybe correlated in future with disability to provide new insights into their functional effects. Supported by: MS Society. Disclosure: Dr. Kearney has nothing to disclose. Dr. Miszkiel has received research support from Biogen Idec and GlaxoSmithKline, Inc. Dr. Yiannakas has nothing to disclose. Dr. Ciccarelli has nothing to disclose. Dr. Miller has received personal compensation for activities with UCL Institute of Neurology, Biogen Idec, GlaxoSmithKline, and Bayer Schering Pharma. Dr. Miller has received personal compensation in an editorial capacity for Journal of Neurology. Dr. Miller has received research support from GlaxoSmithKline, Biogen Idec, and Novartis.

A highly reproducible automated procedure for quantitative analysis of serial brain magnetic resonance (MR) images was developed for use in patients with multiple sclerosis (MS). The intracranial cavity (ICC) was identified on standard dual-echo spin-echo brain MR images using a supervised automated procedure. MR images obtained from one MS patient at 24 time points in the course of a 1-year follow-up were aligned with the images of one of the time points. Next, the contents of the ICC in each MR exam were segmented into four tissues, using a self-adaptive statistical algorithm. Misclassifications due to partial voluming were corrected using a combination of morphologic operators and connectivity criteria. Finally, a connectivity detection algorithm was used to separate the tissue classified as lesions into individual entities. Registration, classification of the contents of the ICC, and identification of individual lesions are fully automatic. Only identification of the ICC requires operator interaction. In each MR exam, the program estimated volumes for the ICC, gray matter (GM), white matter (WM), white matter lesions (WML), and cerebrospinal fluid (CSF). The reproducibility of the system was superior to that of supervised segmentation, as evidenced by the coefficient of variation: CSF supervised 45.9% vs. automated 7.7%, GM 16.0% vs. 1.4%, WM 15.7% vs. 1.3%, and WML 39.5% vs 52.0%. Our results demonstrate that this computerized procedure allows routine reproducible quantitative analysis of large serial MRI data sets.J. Magn. Reson. Imaging 1999;9:519–530. © 1999 Wiley-Liss, Inc.

Should Spinal MRI Be Routinely Performed in Patients With Clinically Isolated Optic Neuritis?

This thesis focuses on finding ways to differentiate the gray matter (GM) and white matter (WM) in magnetic resonance (MR) images of the human spinal cord (SC). The aim of this project is to quantify tissue loss in these compartments to study their implications on the progression of multiple sclerosis (MS). To this end, we propose segmentation algorithms that we evaluated on MR images of healthy volunteers. Segmentation of GM and WM in MR images can be done manually by human experts, but manual segmentation is tedious and prone to intra- and inter-rater variability. Therefore, a deterministic automation of this task is necessary. On axial 2D images acquired with a recently proposed MR sequence, called AMIRA, we experiment with various automatic segmentation algorithms. We first use variational model-based segmentation approaches combined with appearance models and later directly apply supervised deep learning to train segmentation networks. Evaluation of the proposed methods shows accurate and precise results, which are on par with manual segmentations. We test the developed deep learning approach on images of conventional MR sequences in the context of a GM segmentation challenge, resulting in superior performance compared to the other competing methods. To further assess the quality of the AMIRA sequence, we apply an already published GM segmentation algorithm to our data, yielding higher accuracy than the same algorithm achieves on images of conventional MR sequences. On a different topic, but related to segmentation, we develop a high-order slice interpolation method to address the large slice distances of images acquired with the AMIRA protocol at different vertebral levels, enabling us to resample our data to intermediate slice positions. From the methodical point of view, this work provides an introduction to computer vision, a mathematically focused perspective on variational segmentation approaches and supervised deep learning, as well as a brief overview of the underlying project's anatomical and medical background.

Cranial Ultrasonography

In this article, we describe the development and validation of an automatic algorithm to segment brain from extracranial tissues, and to classify intracranial tissues as cerebrospinal fluid (CSF), gray matter (GM), white matter (WM) or pathology. T1 weighted spin echo, dual echo fast spin echo (T2 weighted and proton density (PD) weighted images) and fast Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images were acquired in 100 normal patients and 9 multiple sclerosis (MS) patients. One of the normal studies had synthesized MS-like lesions superimposed. This allowed precise measurement of the accuracy of the classification. The 9 MS patients were imaged twice in one week. The algorithm was applied to these data sets to measure reproducibility. The accuracy was measured based on the synthetic lesion images, where the true voxel class was known. Ninety-six percent of normal intradural tissue voxels (GM, WM, and CSF) were labeled correctly, and 94% of pathological tissues were labeled correctly. A low coefficient of variation (COV) was found (mean, 4.1%) for measurement of brain tissues and pathology when comparing MRI scans on the 9 patients. A totally automatic segmentation algorithm has been described which accurately and reproducibly segments and classifies intradural tissues based on both synthetic and actual images.

Free water fraction (FWF) represents the amount of water per unit volume of brain parenchyma, which is not bound to macromolecules. Its excess in multiple sclerosis (MS) is related to increased tissue loss. The use of mcDESPOT (multicomponent driven single pulse observation of T1 and T2), a 3D imaging method which exploits both the T1 and T2 contrasts, allows FWF to be derived in clinically feasible times. However, this method has not been used to quantify changes of FWF and their potential clinical impact in MS. The aim of this study is to investigate the changes in FWF in MS patients and their relationship with tissue damage and cognition, under the hypothesis that FWF is a proxy of clinically meaningful tissue loss. To this aim, we tested the relationship between FWF, MS lesion burden and information processing speed, evaluated via the Symbol Digit Modalities Test (SDMT). In addition to standard sequences, used for T1- and T2-weighted lesion delineation, the mcDESPOT sequence with 1.7 mm isotropic resolution and a diffusion weighted imaging protocol (b = 0, 1200 s/mm2, 40 diffusion directions) were employed at 3 T. The fractional anisotropy map derived from diffusion data was used to define a subject-specific white matter (WM) atlas. Brain parenchyma segmentation returned masks of gray matter (GM) and WM, and normal-appearing WM (NAWM), in addition to the T1 and T2 lesion masks (T1L and T2L, respectively). Ninety-nine relapsing-remitting MS patients (age = 43.3 ± 9.9 years, disease duration 12.3 ± 7.7 years) were studied, together with twenty-five healthy controls (HC, age = 38.8 ± 11.0 years). FWF was higher in GM and NAWM of MS patients, compared to GM and WM of HC (both p < .001). In MS patients, FWF was the highest in the T1L and GM, followed by T2L and NAWM, respectively. FWF increased significantly with T1L and T2L volume (ρ ranging from 0.40 to 0.58, p < .001). FWF in T2L was strongly related to both T1L volume and the volume ratio T1L/T2L (ρ = 0.73, p < .001). MS patients performed worse than HC in the processing speed test (mean ± SD: 54.1 ± 10.3 for MS, 63.8 ± 10.8 for HC). FWF in GM, T2L, perilesional tissue and NAWM increased with SDMT score reduction (ρ = -0.30, -0.29, -0.33 respectively and r = -.30 for T2L, all with p < .005). A regional analysis, conducted to determine which NAWM regions were of particular importance to explain the relationship between FWF and cognitive impairment, revealed that FWF spatial variance was negatively related to SDMT score in the corpus callosum and the superior longitudinal fasciculus, WM structures known to be associated with cognitive impairment, in addition to the left corticospinal tract, the sagittal stratum, the right anterior limb of internal capsule. In conclusion, we found excess free water in brain parenchyma of MS patients, an alteration that involved not only MS lesions, but also the GM and NAWM, impinging on brain function and negatively associated with cognitive processing speed. We suggest that the FWF metric, derived from noninvasive, rapid MRI acquisitions and bearing good biological interpretability, may prove valuable as an MRI biomarker of tissue damage and associated cognitive impairment in MS.

To prospectively perform a direct measurement of fractional anisotropy (FA) and mean diffusivity (MD) in cortical lesions of patients with multiple sclerosis (MS). The study was approved by the institutional review board and was HIPAA compliant; informed consent was obtained. Magnetic resonance (MR) images, including double inversion-recovery (DIR), phase-sensitive inversion-recovery (PSIR), and diffusion-tensor images, were acquired from nine MS patients with cortical lesions (five women, four men; median age, 47 years) and nine age- and sex-matched volunteer control subjects. Following nonlinear elastically constrained image registration for aligning diffusion-weighted images to DIR images, maps of FA and MD were computed for each subject. Cortical lesions were identified on DIR images and validated by using PSIR images. The diffusion-tensor imaging maps were then overlaid on the coregistered DIR images, and mean FA and MD values were measured in regions of interest drawn on the cortical lesions. Differences between normal gray matter (GM) and cortical lesions were evaluated by using the generalized estimating equation. FA and MD histograms of whole brain and GM (global analysis) in healthy control subjects and MS patients were also computed for comparison with those in previously published studies. FA and MD values were significantly higher in cortical lesions compared with similar regions in healthy control subjects. Histogram peak FA was significantly decreased and peak MD was significantly increased in patients relative to control subjects. DIR and PSIR combined with nonlinear image registration allowed direct focal measurement of FA and MD in cortical lesions.

Background: In natalizumab-treated multiple sclerosis (MS) patients, magnetic resonance imaging (MRI) is considered as a sensitive tool in detecting both MS disease activity and progressive multifocal leukoencephalopathy (PML). Objective: To investigate the performance of neuroradiologists using brain MRI in detecting new MS lesions and asymptomatic PML lesions and in differentiating between MS and PML lesions in natalizumab-treated MS patients. The secondary aim was to investigate interrater variability. Methods: In this retrospective diagnostic study, four blinded neuroradiologists assessed reference and follow-up brain MRI scans of 48 natalizumab-treated MS patients with new asymptomatic PML lesions (n = 21) or new MS lesions (n = 20) or no new lesions (n = 7). Sensitivity and specificity for detection of new lesions in general (MS and PML lesions), MS and PML lesion differentiation, and PML detection were determined. Interrater agreement was calculated. Results: Overall sensitivity and specificity for the detection of new lesions, regardless of the nature of the lesions, were 77.4% and 89.3%, respectively; for PML-MS lesion differentiation, 74.2% and 84.7%, respectively; and for asymptomatic PML lesion detection, 59.5% and 91.7%, respectively. Interrater agreement for the tested categories was fair to moderate. Conclusion: The diagnostic performance of trained neuroradiologists using brain MRI in pharmacovigilance of natalizumab-treated MS patients is moderately good. Interrater agreement among trained readers is fair to moderate.

Oligodendrocyte Progenitor Cell Susceptibility to Injury in Multiple Sclerosis

Multiprotocol MR Image Segmentation in Multiple Sclerosis: Experience with Over 1,000 Studies

Introduction Multiple sclerosis (MS) is a chronic and autoimmune disease that has a significant influence on the central nervous system, such as the brain and spinal cord, affecting millions of individuals globally. Understanding the connection between subcortical brain regions and MS is crucial for effective diagnostic and therapeutic approaches for treating this disabling disease. This study explores the relationship between volume and contours of asymmetry index of subcortical brain regions in individuals with MS using volBrain software (https://www.volbrain.net; developed by José V.Manjón (Valencia Polytechnic University, Valencia, Spain) and Pierrick Coupé (University of Bordeaux, Bordeaux, France)). Methods In our retrospective investigation, we admitted 100 Turkish individuals, comprising 50 patients diagnosed with relapsing-remitting MS (RRMS) (24 (48%) males and 26 (52%) females) and 50 healthy controls (23 (46%) males and 27 (54%) females), registered between October 2017 and February 2022 for five years and underwent assessment in the radiology department at the Teaching and Research Hospital of Kocaeli University; 1,150 Turkish patients were excluded from our study based on our exclusion criteria. We used magnetic resonance imaging with a 3-Tesla (3T) scanner and volBrain software to assess volumes (cm3) and asymmetry indexes due to asymmetry for different levels of atrophy of total intracranial, total brain, gray matter, white matter, and subcortical regions, the most affected regions in MS patients for both patient and control cohorts. Results Statistical analysis revealed a significant difference between patient and control groups (p < 0.001), with patient group mean age at 38.32 years and control group mean age at 32.88 years. Patient group exhibited lower values for total intracranial, total brain, gray matter, white matter, and cerebrospinal fluid volume compared to control group (p < 0.05). The results indicated a statistically significant decrease (p < 0.05) in the values for total intracranial and total brain volume, whereas all other values remained unchanged. We compared volumes of subcortical structures on the right and left sides and found that the putamen, thalamus, and globus pallidus had statistically lower values in the patient group than in the control group (p < 0.001), apart from the lateral ventricle. Furthermore, our retrospective investigation demonstrated a statistically significant difference in the globus pallidus asymmetry index, indicating a preference for the patient group (p < 0.05). A lower asymmetry index value signifies a larger volume for the right side of the subcortical regions of the brain when compared to the left side. Conclusion Brain atrophy, although characterized by irreversible tissue damage, is targeted by therapeutic interventions to prevent progression. It is, therefore, imperative to develop a universally accepted measurement standard for subcortical structures that also considers the inherent variability present within each structure. Our findings serve as an important basis and indicator for the determination of subcortical atrophy and asymmetry in MS, the prognosis of the disease, and the etiology of clinical symptoms. Subsequent research may benefit by adopting the novel approach of considering brain atrophy as an outcome rather than a predictor, thereby facilitating the elucidation of the intricate biological mechanisms that give rise to volume loss.

Background and Purpose: With the increasing prevalence of cerebrovascular risk factors in young adults, accurate differentiation of multiple sclerosis (MS) from cerebral small vessel disease (SVD) remains challenging because of the significant overlap in the appearance of white matter lesions on conventional magnetic resonance (MR) images. The purpose of this study was to use quantitative susceptibility mapping (QSM) to differentiate patients with MS from patients with SVD. Materials and Methods: Thirty-two patients with relapsing-remitting MS and 40 patients with SVD were selected in this institution review board-approved retrospective study. All patient examinations included QSM and T2-weighted FLAIR (T2w). All T2w hyperintense lesions were analyzed on QSM for susceptibility relative to normal-appearing white matter and were evaluated for the presence of hyperintense rims and the central vein sign. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic accuracy of differentiating MS patients from SVD patients. Results: The mean susceptibility value of SVD lesions was significantly lower than MS lesions (2.31 ± 9.68 ppb vs. 27.77 ± 20.27 ppb, p &lt; 0.001). Of MS patients and MS lesions, 68.8% and 22.2% had hyperintense rims on QSM, respectively. Of MS patients and MS lesions, 75% and 37.6% had a central vein sign on QSM, respectively. Lesion susceptibility values allowed accurate differentiation MS from SVD with an area under the ROC curve (AUC) = 0.848 for lesion differentiation, AUC = 0.999 for patient differentiation, and an optimal cut-off susceptibility value of 14.88 ppb. Conclusions: Magnetic susceptibility measured using QSM allows accurate differentiation of MS patients from SVD patients.

There are three parts in MRI projections of which are grey matter formed basically by neurons, white matter formed by axon exremities with mylelins, and cerebrospinal fluid. Changes and damages in these regions can cause various diseases. Autism, Parkinsonism, dyslexia, mental disorders, visual and audial loss can be the examples of grey matter diseases. As for the white matter diseases, MS (multiple sclerosis) and demyelinated diseases, minor cardiovascular diseases, neurologic damages, and blindness can be listed. In this study, a method has been proposed for segmentation of gray matter and white matter regions in the sections of the brain. The proposed method was used to determine the areas and calculate gray, matter matter of two hemispheres in terms of proportions of each other also to all the brain.

Inversion recovery ultrashort echo time magnetic resonance imaging: A method for simultaneous direct detection of myelin and high signal demonstration of iron deposition in the brain – A feasibility study