MRI and multiple sclerosis––the evolving role of MRI in the diagnosis and management of MS: the radiologist’s perspective

Detection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learning

In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early or distinct disease processes. The role of MRI-observed DAWM is understudied due to a lack of automated assessment methods. Supervised deep learning (DL) methods are highly capable in this domain, but require large sets of labeled data. To overcome this challenge, a DL-based network (DAWM-Net) was trained using semi-supervised learning on a limited set of labeled data for segmentation of DAWM, focal lesions, and normal-appearing brain tissues on multiparametric MRI. DAWM-Net segmentation performance was compared to a previous intensity thresholding-based method on an independent test set from expert consensus (N = 25). Segmentation overlap by Dice Similarity Coefficient (DSC) and Spearman correlation of DAWM volumes were assessed. DAWM-Net showed DSC > 0.93 for normal-appearing brain tissues and DSC > 0.81 for focal lesions. For DAWM-Net, the DAWM DSC was 0.49 ± 0.12 with a moderate volume correlation (ρ = 0.52, p < 0.01). The previous method showed lower DAWM DSC of 0.26 ± 0.08 and lacked a significant volume correlation (ρ = 0.23, p = 0.27). These results demonstrate the feasibility of DL-based DAWM auto-segmentation with semi-supervised learning. This tool may facilitate future investigation of the role of DAWM in MS.

Spine MRI: A Review of Commonly Encountered Emergent Conditions

Over the last 2 decades, the proliferation of magnetic resonance imaging (MRI) availability and continuous improvements in acquisition speeds have led to significantly increased MRI utilization across the health care system, and MRI studies are increasingly ordered in the emergent setting. Depending on the clinical presentation, MRI can yield vital diagnostic information not detectable with other imaging modalities. The aim of this text is to report on the up-to-date indications for MRI of the spine in the ED, and review the various MRI appearances of commonly encountered acute spine pathology, including traumatic injuries, acute non traumatic myelopathy, infection, neoplasia, degenerative disc disease, and postoperative complications. Imaging review will focus on the aspects of the disease process that are not readily resolved with other modalities.

MRI in Neuromuscular Diseases: An Emerging Diagnostic Tool and Biomarker for Prognosis and Efficacy

There is an unmet need to identify biomarkers sensitive to change in rare, slowly progressive neuromuscular diseases. Quantitative magnetic resonance imaging (MRI) of muscle may offer this opportunity, as it is noninvasive and can be carried out almost independent of patient cooperation and disease severity. Muscle fat content correlates with muscle function in neuromuscular diseases, and changes in fat content precede changes in function, which suggests that muscle MRI is a strong biomarker candidate to predict prognosis and treatment efficacy. In this paper, we review the evidence suggesting that muscle MRI may be an important biomarker for diagnosis and to monitor change in disease severity. ANN NEUROL 2020;88:669-681.

Optic Chiasm Morphometric Changes in Multiple Sclerosis: Feasibility of a Simplified Brain Magnetic Resonance Imaging Measure of White Matter Atrophy

Sophisticated volume measurements of brain structures on magnetic resonance imaging (MRI) may improve specificity in determining long-term progression of multiple sclerosis (MS), but these techniques are laborious. The optic chiasm (OC) is a white matter (WM) structure clearly visible on a routine MRI and is related to the optic nerves (ONs), which are known to atrophy in MS. We hypothesized that OC morphometric measurements would show OC atrophy in MS compared to normal patients. If so, this could help establish a novel simplified brain MRI measure of WM atrophy in MS patients. We retrospectively evaluated standard brain MRIs of 97 patients with known MS and 98 normal individuals. We electronically measured eight OC morphometrics on axial T2WIs and midsagittal T1WIs: OC width and anteroposterior (AP) diameter, diameters of each ON and optic tract (OT), and angles between the ONs or OTs. Mean OC width, AP diameter, and height in MS patients were 11.83 ± 1.25 mm (95% CI 11.58-12.09), 2.99 ± 0.65 mm (95% CI 2.85-3.12), and 2.09 ± 0.37 mm (95% CI 2-2.19), respectively. In normal individuals, they were 12.1 ± 1.4 mm (95% CI 11.78-12.34), 3.43 ± 0.63 mm (95% CI 3.3-3.58), and 2.15 ± 0.37 mm (95% CI 2.07-2.23), respectively. There were statistically significant differences between MS patients and controls for AP diameter (P = 0.000), but not for width (P = 0.204) or height (P = 0.183). The ONs were significantly smaller in MS (P < 0.0017), but not the OTs. Thus, the OC is significantly atrophied in an unstratified cohort of MS patients. Future studies may establish an MRI OC morphometric index to evaluate demyelinating disease in the brain. Clin. Anat. 32:1072-1081, 2019. © 2019 Wiley Periodicals, Inc.

Quantitative magnetic resonance assessment of brain atrophy related to selected aspects of disability in patients with multiple sclerosis: preliminary results

Purpose

The aim of this volumetric study was to evaluate the relationship between brain atrophy quantification in multiple sclerosis (MS) patients and the progression of disability measured by neurological standardised tests.

Material and methods

Seventeen patients (mean age 40.89 years) with clinically definite MS and 24 control subjects (mean age 38.45 years) were enrolled in the study. Brain examinations were performed on a 1.5T MR scanner. Automatic brain segmentation was done using FreeSurfer. Neurological disability was assessed in all patients in baseline and after a median follow-up of two years, using EDSS score evaluation.

Results

In MS patients we found significantly (p < 0.05) higher atrophy rates in many brain areas compared with the control group. The white matter did not show any significant rate of volume loss in MS patients compared to healthy controls. Significant changes were found only in grey matter volume in MS subjects. At the follow-up evaluation after two years MS patients with deterioration in disability revealed significantly decreased cerebral volume in 14 grey matter areas at baseline magnetic resonance imaging (MRI) compared to MS subjects without disability progression.

Conclusions

Grey matter atrophy is associated with the degree of disability in MS patients. Our results suggest that morphometric measurements of brain volume could be a promising non-invasive biomarker in assessing the volumetric changes in MS patients as related to disability progression in the course of the disease.

A Review on a Deep Learning Perspective in Brain Cancer Classification

A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.