Quantitative follow‐up of patients with multiple sclerosis using MRI: Technical aspects

This paper presents a methodology for segmenting PD- and T2-weighted brain magnetic resonance (MR) images of multiple sclerosis (MS) patients into white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and MS lesions. For a given vectorial image (with PD- and T2-weighted components) to be segmented, we perform first intensity inhomogeneity correction and standardization prior to segmentation. Absolute fuzzy connectedness and certain morphological operations are utilized to generate the brain intracranial mask. The optimum thresholding method is applied to the product image (the image in which voxel values represent T2 value x PD value) to automatically recognize potential MS lesion sites. Then, the recently developed technique -- vectorial scale-based relative fuzzy connectedness -- is utilized to segment all voxels within the brain intracranial mask into WM, GM, CSF, and MS lesion regions. The number of segmented lesions and the volume of each lesion are finally output as well as the volume of other tissue regions. The method has been tested on 10 clinical brain MRI data sets of MS patients. An accuracy of better than 96% has been achieved. The preliminary results indicate that its performance is better than that of the k-nearest neighbors (kNN) method.

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

We assessed the occurrence, extent, and frequency of formation of cortical lesions (CLs) in patients with relapsing-remitting (RR) and secondary progressive (SP) multiple sclerosis (MS), and their relationship with cortical atrophy and disability progression. One-hundred seven MS patients (76 RRMS and 31 SPMS), enrolled in a prospective, longitudinal magnetic resonance imaging (MRI) study, were assessed clinically and by brain MRI (including a double inversion recovery sequence) 3 years after study initiation. CL number and volume, T2 white matter (WM) lesion volume, gray matter fraction, and expanded disability status scale (EDSS) were measured. At baseline, CLs were detected in 64.4% of RRMS and 74.2% of SPMS patients. During follow-up, 132 new CLs were found in 44 RRMS patients (57.9%; 0.8 new CL/patient/yr) and 61 in 15 SPMS patients (48.4%; 1.0 new CL/patient/yr). Among these patients, only 31 also showed at least 1 new WM lesion. CL number and volume increases were higher in the 52 patients with a clinical worsening compared with those without (p < 0.001). Baseline CL volume correlated with baseline EDSS (r = 0.36, p < 0.001) and EDSS changes over time (r = 0.51, p < 0.001). Baseline CL volume was an independent predictor of EDSS accumulation and GM volume change at follow-up in both patient groups. In SPMS patients, baseline T2 WM lesion volume was another independent predictor of EDSS worsening. In relapse-onset MS, CLs accumulate over time and are associated with disability progression. The quantification of CLs might represent an additional useful paraclinical tool to monitor MS evolution.

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.

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

Background: Structural brain damage, especially to white matter, is well documented in chronic alcohol abuse. There is also evidence for brain metabolic abnormalities in this condition. It is unknown, however, to what extent these structural and metabolic changes are present in treated alcohol abusers who achieve long-term abstinence versus treatment-naïve, heavily drinking individuals. Methods: This study compared 12 recovering alcoholics with 8 actively heavily drinking subjects. Participants underwent magnetic resonance (MR) imaging and proton MR spectroscopic imaging of the brain. Semiautomated image segmentation techniques yielded volumes for gray matter, white matter, white matter lesions, and cerebral spinal fluid in multiple brain regions defined by Talairach stereotaxic coordinates. Automated spectral processing methods yielded gray and white matter concentrations of the metabolites N-acetylaspartate, creatine, and choline for the same regions. Results: Recovering alcoholics had greater volumes of frontal white matter, but the opposite was true for white matter in a “remainder” region encompassing the basal frontal and temporal lobes, the cerebellum, and the brainstem. Recovering alcoholics also had smaller volumes of white matter lesions in whole brain, in occipital and mesial parietal regions, and in the remainder region. Recovering alcoholics had greater gray matter volumes in the orbital frontal pole and postcentral gyrus, but smaller gray matter volumes in the anterior cingulate. Whole-brain and regional metabolite concentrations did not differ significantly between the two groups. Conclusions: White and gray matter volumes in different regions of the brain were greater or smaller in recovering, treated alcoholics. The findings suggest region-specific structural recovery from chronic alcohol–induced brain injury, but also region-specific long-term structural damage in abstinent alcoholics. White matter lesions were widespread in active drinkers and may partly resolve during long-term abstinence. Proton MR spectroscopic measures, as applied in this cross-sectional study, were largely ineffective in revealing metabolic effects of abstinence on the alcohol-damaged brain.

In multiple sclerosis (MS), using simultaneous magnetic resonance-positron emission tomography (MR-PET) imaging with 11 C-PBR28, we quantified expression of the 18kDa translocator protein (TSPO), a marker of activated microglia/macrophages, in cortex, cortical lesions, deep gray matter (GM), white matter (WM) lesions, and normal-appearing WM (NAWM) to investigate the in vivo pathological and clinical relevance of neuroinflammation. Fifteen secondary-progressive MS (SPMS) patients, 12 relapsing-remitting MS (RRMS) patients, and 14 matched healthy controls underwent 11 C-PBR28 MR-PET. MS subjects underwent 7T T2*-weighted imaging for cortical lesion segmentation, and neurological and cognitive evaluation. 11 C-PBR28 binding was measured using normalized 60- to 90-minute standardized uptake values and volume of distribution ratios. Relative to controls, MS subjects exhibited abnormally high 11 C-PBR28 binding across the brain, the greatest increases being in cortex and cortical lesions, thalamus, hippocampus, and NAWM. MS WM lesions showed relatively modest TSPO increases. With the exception of cortical lesions, where TSPO expression was similar, 11 C-PBR28 uptake across the brain was greater in SPMS than in RRMS. In MS, increased 11 C-PBR28 binding in cortex, deep GM, and NAWM correlated with neurological disability and impaired cognitive performance; cortical thinning correlated with increased thalamic TSPO levels. In MS, neuroinflammation is present in the cortex, cortical lesions, deep GM, and NAWM, is closely linked to poor clinical outcome, and is at least partly linked to neurodegeneration. Distinct inflammatory-mediated factors may underlie accumulation of cortical and WM lesions. Quantification of TSPO levels in MS could prove to be a sensitive tool for evaluating in vivo the inflammatory component of GM pathology, particularly in cortical lesions. Ann Neurol 2016;80:776-790.

Objective: To compare the differences of texture characteristic among macroscopic lesion white matter(LWM), normal appearing white matter(NAWM) in magnetic resonance images (MRI) from patients with multiple sclerosis(MS) and normal white matter(NWM) from normal controls by synthesized gray level co-occurrence matrix. Methods: T2-weighted MRI of 26 MS patients and 26 healthy subjects were selected, and 26 regions of interests (ROI) were chosen from MS patient MRI and healthy subject MRI for LWM, NAWM and NWM respectively. All of the ROI were analyzed by synthesized gray level co-occurrence matrix, and energy, inertia, inverse difference moment and entropy were extracted. Finally statistic significance was tested among three groups. Results: In LWM, NAWM and NWM groups, the increasing tendency was shown for energy (0.0032, 0.0048 and 0.0069) and inverse difference moment (0.2199, 0.3246 and 0.3706) , while decreasing tendency for inertia (79.0494, 18.6403 and 9.8744) and entropy (2.7453,2.4571 and 2.3092) . Except inertia that the difference was not significant (P=0.251) between NAWM and NWM , the rests had significant differences (P<0.05)between LWM and NAWM (or NWM) or NAWM and NWM. Conclusion: Texture analysis can possible reveal the microscopic abnormality in patients with MS, and will be beneficial to the research of early diagnosis and treatment estimate of MS.KeywordsMultiple SclerosisMultiple Sclerosis PatientExpand Disability Status ScaleLesion White MatterMultiple Sclerosis LesionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

To examine relationships between brain white matter hyperintensities depicted at magnetic resonance (MR) imaging and performance on neuropsychologic tests in community-dwelling elderly adults. The 1921 Aberdeen Birth Cohort is a subsample of survivors of the Scottish Mental Survey of 1932 whose mental ability was tested at 11 years of age. Ninety-five of these subjects agreed to undergo brain MR imaging, an examination of general health, and a neuropsychologic evaluation. White matter hyperintensities detected at T2-weighted MR imaging were rated by using a semiquantitative method yielding two continuous variables: white matter lesions and periventricular lesions. Cognitive ability, including crystallized and fluid intelligence domains, was assessed with standard neuropsychologic tests. Rating scores of white matter lesions were normally distributed (on a devised scale) with means of 1.14 for white matter lesions and 1.28 for periventricular lesions. Intra- and interobserver reliability coefficients for scores were high, generally above 0.7. There were significant correlations of medium effect size between the T2-weighted MR imaging-depicted white matter lesions and performance on tests of fluid-type intelligence. No significant correlation was demonstrated between white matter lesion ratings and tests of crystallized intelligence. Lower fluid-type ("prevailing") intelligence test scores were associated with increased severity of white matter lesion ratings but not crystallized-type ("premorbid") intelligence test scores. This indicates that MR imaging-depicted white matter lesions are of clinical importance.

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.

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.

To the Editor: The recent article by DeCarli et al1 addresses a somewhat neglected aspect of white matter hyperintensities (WMH), the significance of their anatomical location. The authors argue that the commonly accepted categorization into deep (DWMH) and periventricular (PVWMH) WMH is arbitrary, because the 2 are very highly correlated, and a spatial analysis does not reveal distinct populations. We think that this conclusion is premature, because the categorization depends on a number of factors. The first limitation of their analysis is that they examined individuals in their 70s who presented to a specialty clinic, suggesting that the white matter lesions in their sample were at an advanced stage. If an analogy is drawn from cerebral atrophy in dementia, regional differences in atrophy that are present in the different subtypes of dementia become less prominent in the later stages. In our study of WMH in middle age (60 to 64 years), the correlation of DWMH and PVWMH was much lower ( r =0.621; …

White matter hyperintensities (WMHs), presented on T2-weighted or fluid-attenuated inversion recovery magnetic resonance imaging (MRI) sequences, are lesions in the human brain that can be observed in both migraine and multiple sclerosis (MS). Seventeen migraine patients and 15 patients with relapsing-remitting multiple sclerosis with WMHs, and 17 healthy subjects age-and sex-matched to the migraine group were prospectively enrolled and underwent conventional and advanced MRI studies with diffusion-and perfusion-weighted imaging and single voxel proton magnetic resonance spectroscopy. In both disease groups, elevated T2 relaxation time, apparent diffusion coefficient (ADC) values, and decreased N-acetyl-aspartate levels were found in the intralesional white matter compared to the contralateral normal-appearing white matter (NAWM), while there was no difference between the hemispheres of the control subjects. Migraine patients had the lowest intralesional creatine + phosphocreatine and myo-inositol (mI) values among the three groups, while patients with MS showed the highest intralesional T1 and T2 relaxation times, ADC, and mI values. In the contralateral NAWM, the same trend with mI changes was observed in migraineurs and MS patients. No differences in perfusion variables were observed in any groups. Our multimodal study showed that tissue damage is detectable in both diseases. Despite the differences in various advanced MRI measures, with more severe injury detected in MS lesions, we could not clearly differentiate the two white matter lesion types.

MR spectroscopic imaging of glutathione in the white and gray matter at 7 T with an application to multiple sclerosis