Multiple Sclerosis Lesion Volume Research Articles

An Efficient Multiple Sclerosis Segmentation Framework using Hybrid Dilated Convolution –based Adaptive Mobilenet Mechanism.

Multiple Sclerosis (MS) is considered a very popular neurological condition in adults. Poor walking stability is considered the primary sign of MS. The Magnetic Resonance Imaging (MRI) is ensured to be a delicate approach for discovering disease progression. Severity quantification of the disease via MS lesion volume validation with MR imaging is significant for monitoring and learning the disorder and its treatment. Numerous techniques for MS lesions segmentation demand experts seed points as input, yet do not sufficiently permit the specialists to perform effectively or intuitively. Interestingly, various approaches also consider that the points are optimally determined. Hence, it is necessary to rectify some of the troubles that existed in classical MS classification mechanisms by developing a powerful process with deep learning in this work. In the beginning, the requisite MS images are fetched from the existing online source and forwarded to the module of image segmentation. In this module, an efficient image segmentation operation is carried out employing Transformer Unet++ with MobileNetv3 (TUnet++-MNetv3). Further, the segmented images are given to the module named MS classification, where the Hybrid Dilated Convolution based Adaptive MobileNet (HDC-AMNet) is recommended to classify the MS. Moreover, the Enhanced Single Candidate Optimizer (ESCO) is adopted to optimize the parameters that exist in the MobileNet. Thus, the designed MS classification task achieves improved performance rates by contrasting it with its baseline techniques employing numerous metrics.

Multiple sclerosis lesions that impair memory map to a connected memory circuit.

Nearly 1 million Americans are living with multiple sclerosis (MS) and 30-50% will experience memory dysfunction. It remains unclear whether this memory dysfunction is due to overall white matter lesion burden or damage to specific neuroanatomical structures. Here we test if MS memory dysfunction is associated with white matter lesions to a specific brain circuit. We performed a cross-sectional analysis of standard structural images and verbal memory scores as assessed by immediate recall trials from 431 patients with MS (mean age 49.2 years, 71.9% female) enrolled at a large, academic referral center. White matter lesion locations from each patient were mapped using a validated algorithm. First, we tested for associations between memory dysfunction and total MS lesion volume. Second, we tested for associations between memory dysfunction and lesion intersection with an a priori memory circuit derived from stroke lesions. Third, we performed mediation analyses to determine which variable was most associated with memory dysfunction. Finally, we performed a data-driven analysis to derive de-novo brain circuits for MS memory dysfunction using both functional (n = 1000) and structural (n = 178) connectomes. Both total lesion volume (r = 0.31, p < 0.001) and lesion damage to our a priori memory circuit (r = 0.34, p < 0.001) were associated with memory dysfunction. However, lesion damage to the memory circuit fully mediated the association of lesion volume with memory performance. Our data-driven analysis identified multiple connections associated with memory dysfunction, including peaks in the hippocampus (T = 6.05, family-wise error p = 0.000008), parahippocampus, fornix and cingulate. Finally, the overall topography of our data-driven MS memory circuit matched our a priori stroke-derived memory circuit. Lesion locations associated with memory dysfunction in MS map onto a specific brain circuit centered on the hippocampus. Lesion damage to this circuit fully mediated associations between lesion volume and memory. A circuit-based approach to mapping MS symptoms based on lesions visible on standard structural imaging may prove useful for localization and prognosis of higher order deficits in MS.

Multiple sclerosis lesion detection in multimodal MRI using simple clustering-based segmentation and classification

Abstract Background Multiple sclerosis (MS) is an immune-mediated inflammatory disease that attacks myelinated axons in the central nervous system, destroying myelin and axons to variable degrees and, resulting in significant physical disability. Magnetic resonance imaging (MRI) is useful in the diagnosis of MS, surpassing all other imaging techniques in terms of prediction accuracy. Depending on the number and location of lesions, however, the success of MR can vary significantly in terms of sensitivity and specificity in the diagnosis of MS. Adverse effects of various intensity and residual artifacts in the MRI data make it challenging to compute MS lesion volume to assess the progression of MS. Therefore, the development of robust and automated MS lesion detection methods has been a challenge. Objectives This study aims to develop a novel, robust, and simple image segmentation method to perform quantitative analysis of MS lesions from multimodal MRI data. Methods An algorithm based on a supervised minimum Euclidean distance-based clustering method employing three 2D MRI modalities, T1-weighted (T1w), fluid-attenuated inversion recovery (FLAIR), and T2-weighted (T2w) MRI was developed for classification of significant brain tissues and MS lesions. The developed method was applied to an MRI dataset from six MS patients. Results The developed method classifies various brain tissues and detects MS lesions with over 90% accuracy and specificity, and 62%–65% sensitivity, on average. Conclusions Segmentation of different brain tissues using our proposed algorithm results in superior MS lesion-detection accuracy, comparable with the recent deep-learning classification results in the literature.

An adaptive sparse Bayesian model combined with probabilistic label fusion for multiple sclerosis lesion segmentation in brain MRI

This paper presents a novel method for automatic segmentation of multiple sclerosis (MS) lesion in brain magnetic resonance (MR) image. The first research directly focuses on the development of an algorithm called the adaptive sparse Bayesian decision theorem (ASBDT), which integrates Markov random field theorem and Gibbs random field theorem to segment MS lesion in T1-w, T2-w and FLAIR images, respectively. The second research focus is based on a probabilistic label fusion (PLF) algorithm, which utilizes a set of prior known multi-atlas obtained from T1-w, T2-w and FLAIR images, respectively. The PLF algorithm is based on local weighted voting strategy, in which a specific coefficient obtained using the maximum likelihood function between the prior known atlas and the target image is assigned as the weight to individual atlas. The proposed segmentation approach is evaluated by 20 brain MR image data sets from the MICCAI MS Lesion Segmentation Challenge. The values of evaluation parameters TPR, PPV, DSC indicate that our approach is better than some other methods. According to the McDonald Criteria updated in 2017, the number and volume is a key point in defining the disease, we calculate the total MS lesion volume and number. Therefore, this novel approach has an added value for the clinical evaluation of MS lesion patients.

Insular multiple sclerosis lesions are associated with erectile dysfunction

Erectile function (EF) is frequently compromised in men with multiple sclerosis (MS). Functional neuroimaging in healthy men identified a network of brain areas, such as the insula, visual and somatosensory association areas, cingulate gyrus, prefrontal cortex, as well as subcortical regions, contributing to EF. This study intended to determine associations between EF deterioration during MS and cerebral MS-associated lesion sites. In 31 men with MS (mean age 38.2±11.2years), we evaluated MS-related EF deterioration by comparing scores of the 5-item International Index of Erectile Function-5 questionnaire (IIEF5) at the time of study and retrospectively, 3months prior to MS diagnosis, by calculating score differences as DeltaIIEF5 (DeltaIIEF5 score<0 indicated EF deterioration). To assess the impact of confounding factors of EF, patient age, disease duration, disease severity, depressiveness, bladder and bowel symptoms, and total cerebral MS lesion volume were correlated with DeltaIIEF5 scores (Spearman rank correlation) and compared between patients with and without EF deterioration(t tests or Mann-Whitney U test). MS lesions were assessed on T2-weighted magnetic resonance imaging (MRI; 1.5 or 3T). We determined the lesion overlap (prevalence of identical lesion sites among patients), subtracted lesion overlaps in patients without EF deterioration from overlaps in patients with EF deterioration, and compared DeltaIIEF5 scores voxel-wise between patients with and without lesions in a given voxel (t test; significance: p<0.05). In 14 patients (45.2%), DeltaIIEF5 scores indicated EF deterioration. DeltaIIEF5 scores were not associated with age (ρ=0.06; p=0.74), disease duration (ρ=0.26; p=0.15), disease severity (ρ=-0.19; p=0.31), depressiveness (ρ=0.07; p=0.72), bladder symptoms (ρ=-0.11; p=0.57), bowel symptoms (ρ=0.17; p=0.37), and total lesion volume (ρ=-0.13; p=0.47). The voxel-wise analysis showed associations between EF deterioration and MS lesions primarily in the bilateral, and predominantly left juxtacortical insular region. In conclusion, MS lesions particularly in the left insular region, which is activated with sexual arousal, contribute to erectile dysfunction.

Association of neuropathic limb pain in multiple sclerosis with cognition, behaviour, and measures of brain structure: a case-control MRI neuroimaging study

BackgroundNeuropathic limb pain commonly affects people with multiple sclerosis but is incompletely understood. Emotional, psychological, and executive functions, acting via brainstem pathways, have been linked to pain modulation. We aimed to compare these functions, as well as MRI measures of brain structure, in adults with multiple sclerosis with and without pain. We hypothesised that, in those with pain, the ability to suppress or reappraise stimuli would be reduced, with altered lesion distribution or grey matter volume loss. MethodsAdults with relapsing remitting multiple sclerosis with and without neuropathic limb pain were recruited from clinic; they were matched for sex, age, disability, duration of multiple sclerosis, and education. None used strong opioids. Participants underwent targeted psychological and neuropsychological assessment, with 1 mm3 MPRAGE (magnetisation-prepared rapid gradient-echo), T2, and FLAIR (fluid attenuation inversion recovery) brain MRI at 3 Tesla. Distribution of multiple sclerosis lesions was analysed by semi-automated segmentation, probability mapping, and permutation analysis. Grey matter volume was analysed by voxel based morphometry (VBM). Significance thresholds adjusted for multiple comparisons. FindingsWe recruited 31 adults with pain and 16 matched controls. Participants with pain more often used adjuvant analgesics than did controls (22/31 [71·0%] vs 3/16 [18·8%], p=0·002). They described more depressive symptoms (Hospital Depression Scale median 5·0 [IQR 2·0–8·5] vs 1·5 [0·0–6·0], p=0·005), fatigue (Fatigue Severity Scale 51·0 [43·5–56·5] vs 33·0 [26·8–43·8], p=0·003), and catastrophising (Pain Catastrophizing Scale 16·0 [12·5–25·0] vs 8·5 [0·8–15·0], p=0·004). Patients with pain displayed impaired reappraisal (Delis Kaplan card test set 1 recognition scores 12·0 [8·0–20·0] vs 24·0 [21·0–24·0], p<0·0001). Multiple sclerosis lesion volume overall did not significantly differ between groups. Brainstem lesion volume was significantly higher in those with pain (p=0·0049). VBM did not reveal altered cortical volumes between groups. InterpretationThis cross-sectional study suggests that neuropathic limb pain in relapsing remitting multiple sclerosis is associated with specific emotional, psychological, and executive dysfunction, and brainstem location of lesions. Adjuvant analgesics can affect neuropsychological performance, but are less likely to affect imaging measures. Longitudinal or interventional studies could clarify any mechanistic or therapeutic implications of our findings. FundingAnne Rowling Regenerative Neurology Clinic, University of Edinburgh (Rowling Scholar scheme); University of Edinburgh (Doreen Maguire endowment).

Design and development of an ethnically-diverse imaging informatics-based eFolder system for multiple sclerosis patients

PurposeMRI has been used to identify multiple sclerosis (MS) lesions in brain and spinal cord visually. Integrating patient information into an electronic patient record system has become key for modern patient care in medicine in recent years. Clinically, it is also necessary to track patients’ progress in longitudinal studies, in order to provide comprehensive understanding of disease progression and response to treatment. As the amount of required data increases, there exists a need for an efficient systematic solution to store and analyze MS patient data, disease profiles, and disease tracking for both clinical and research purposes. MethodAn imaging informatics based system, called MS eFolder, has been developed as an integrated patient record system for data storage and analysis of MS patients. The eFolder system, with a DICOM-based database, includes a module for lesion contouring by radiologists, a MS lesion quantification tool to quantify MS lesion volume in 3D, brain parenchyma fraction analysis, and provide quantitative analysis and tracking of volume changes in longitudinal studies. Patient data, including MR images, have been collected retrospectively at University of Southern California Medical Center (USC) and Los Angeles County Hospital (LAC). The MS eFolder utilizes web-based components, such as browser-based graphical user interface (GUI) and web-based database. The eFolder database stores patient clinical data (demographics, MS disease history, family history, etc.), MR imaging-related data found in DICOM headers, and lesion quantification results. Lesion quantification results are derived from radiologists’ contours on brain MRI studies and quantified into 3-dimensional volumes and locations. Quantified results of white matter lesions are integrated into a structured report based on DICOM-SR protocol and templates. The user interface displays patient clinical information, original MR images, and viewing structured reports of quantified results. The GUI also includes a data mining tool to handle unique search queries for MS. System workflow and dataflow steps has been designed based on the IHE post-processing workflow profile, including workflow process tracking, MS lesion contouring and quantification of MR images at a post-processing workstation, and storage of quantitative results as DICOM-SR in DICOM-based storage system. The web-based GUI is designed to display zero-footprint DICOM web-accessible data objects (WADO) and the SR objects. SummaryThe MS eFolder system has been designed and developed as an integrated data storage and mining solution in both clinical and research environments, while providing unique features, such as quantitative lesion analysis and disease tracking over a longitudinal study. A comprehensive image and clinical data integrated database provided by MS eFolder provides a platform for treatment assessment, outcomes analysis and decision-support. The proposed system serves as a platform for future quantitative analysis derived automatically from CAD algorithms that can also be integrated within the system for individual disease tracking and future MS-related research. Ultimately the eFolder provides a decision-support infrastructure that can eventually be used as add-on value to the overall electronic medical record.

Do multiple sclerosis lesions affect the outcome of magnetic resonance image registration?

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system manifested morphologically by inflammation, demyelination, axonal loss and gliosis. The Magnetic Resonance (MR) imaging, which presents high resolution and good differentiation between brain tissues, is considered the gold standard for the detection and evolution assessment of MS lesions. Fairly recently, a number of automatic image processing systems have been proposed in the literature to help radiologists detect and compute the volume of MS lesions in MR images. Among most of these approaches, registration of multi-contrast MR clinical images (T1/T2/PD/FLAIR) as well as the registration of patient’s data to anatomical atlases have proved to be essential steps in the neuroimaging processing pipeline to successfully detect and quantitatively assess lesion load in MS. However, despite its importance and common use in automatic image processing systems, the effect of MS lesions on the final results of image registration has not been thoroughly investigated. In this work, image registration techniques using both affine and deformable transformations were used to assess if MS lesions (stratified in mild, moderate or severe) had any affect on the final results of image registration. Based on quantitative results obtained using metrics such as Dice and volume overlap, it was verified that MS lesions do not significantly affect the registration process. In the severe lesions case, for example, the Dice metric gave results of 0.999 and 0.963 for affine and deformable transformation, respectively. Also, it was verified that the sole use of affine transformation is very reasonable to correctly align images even if they do have lesions.

Latency of multifocal visual evoked potentials in nonoptic neuritis eyes of multiple sclerosis patients associated with optic radiation lesions.

The aim of the study was to test the hypothesis that latency delay of multifocal visual evoked potentials (mfVEP) in nonoptic neuritis (NON) eyes of multiple sclerosis (MS) patients is related to retrochiasmal demyelinating lesions. A total of 57 MS patients with no history of optic neuritis at least in one eye, and 25 age- and sex-matched healthy controls was enrolled. Probabilistic tractography was used to reconstruct optic radiation (OR) fibers. The MS lesion volume within and outside of OR was calculated. Diffusion tensor imaging (DTI) indices were measured along OR fibers. The relationship of the mfVEP latency with OR lesions and DTI indices was examined. Average mfVEP latency in the MS cohort was significantly delayed compared to controls (P < 0.0001). Of the patients, 77% demonstrated OR lesions. Axial, radial, and mean diffusivity were significantly abnormal in MS patients (P < 0.001). Partial correlation demonstrated significant association between mfVEP latency delay and OR lesion load. There was also significant correlation between MfVEP latency and OR DTI. Subgroup analysis revealed significantly higher correlations in patients without a history of ON in either eye compared to the fellow eye of patients with previous ON. The findings of this study support our hypothesis that latency delay of the mfVEP in eyes of MS patients without previous ON is related to retrogenicular demyelinating lesions. Additionally, this study demonstrated that a previous episode of ON in the fellow eye may be a significant confounding factor, masking the relationship between the latency and OR lesions.

Location and volume of multiple sclerosis lesions predict enhanced or decreased female sexual function

Location and volume of multiple sclerosis lesions predict enhanced or decreased female sexual function

Enhanced or Decreased Female Sexual Function Depends on Location and Volume of Multiple Sclerosis Lesions (P06.111)

Enhanced or Decreased Female Sexual Function Depends on Location and Volume of Multiple Sclerosis Lesions (P06.111)

Conventional and Quantitative Magnetic Resonance Imaging and Cognitive Impairment in Multiple Sclerosis

An estimated 30–70% of patients with multiple sclerosis (MS) have some cognitive impairment. Cognitive function depends on a spectrum of faculties including information processing speed, sustained attention, recent memory and executive function. The broad definition of cognition has resulted in different assessments of function, and repeatable batteries of tests have been devised to gain an overall and repeatable view of cognition in MS. Many studies have attempted to find an association between cognitive function and MS pathology using magnetic resonance imaging (MRI). Conventional MRI has been used to show the relationship between cognitive impairment and MS lesion volume and/or brain volume (reflecting atrophy). Studies using quantitative MRI to estimate the degree of abnormality in tissue that is normal-appearing on conventional MRI have also found correlations with cognitive function. Longitudinally, cognitive decline has been found to correlate with changing MRI-detectable pathology in some studies. However, consistency between studies has been lacking, and the large number of cognitive tests available makes direct comparison of different studies difficult. Focus on specific cognitive domains may alleviate this issue in future studies.

Regional magnetic resonance imaging lesion burden and cognitive function in multiple sclerosis: a longitudinal study.

To investigate the relationship between magnetic resonance imaging regional lesion burden and cognitive performance in multiple sclerosis (MS) over a 4-year follow-up period. Twenty-eight patients with MS underwent magnetic resonance imaging and took the Brief, Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis at baseline, 1-year, and 4-year follow-up. An automated 3-dimensional lesion detection method was used to identify MS lesions within anatomical regions on proton density T2-weighted images. The relationship between magnetic resonance imaging regional lesion volumes and the Brief, Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis results was examined using regression analyses. At all time points, frontal lesion volume represented the greatest proportion of total lesion volume, and the percentage of white matter classified as lesion was also highest in frontal and parietal regions. On neuropsychological testing, when compared with age- and educational level-matched control subjects, patients with MS showed significant impairment on tests of sustained attention, processing speed, and verbal memory (P<.001). Performance on these measures was negatively correlated with MS lesion volume in frontal and parietal regions at baseline, 1-year, and 4-year follow-up (R = -0.55 to -0.73, P<.001). Multiple sclerosis lesions show a propensity for frontal and parietal white matter. Lesion burden in these areas was strongly associated with performance on tasks requiring sustained complex attention and working verbal memory. This relationship was consistent over a 4-year period, suggesting that disruption of frontoparietal subcortical networks may underlie the pattern of neuropsychological impairment seen in many patients with MS.

Correlations between monthly enhanced MRI lesion rate and changes in T2 lesion volume in multiple sclerosis.

Magnetic resonance imaging (MRI) provides a powerful tool for assessing disease activity in multiple sclerosis (MS), and its role as a surrogate marker for monitoring treatment efficacy is now becoming established. The most commonly used MRI parameters in treatment trials are (1) monthly gadolinium-enhanced MRI, with the number of active lesions serving as the outcome measure, and (2) annual lesion load quantification, in which change in MS lesion volume provides the MRI endpoint. We evaluated clinical/MRI correlations and the relationship between these two markers of disease activity in 73 patients with clinically definite MS. Quantification of T2 lesion load was performed at study entry and exit, with a median study duration of 11 months (range, 9 to 14 months). Monthly postgadolinium T1-weighted images were acquired between these time points. Lesion load at study entry was significantly correlated with the baseline Expanded Disability Status Scale (EDSS) score, but no significant longitudinal correlation was demonstrated. The number of enhancing lesions on the entry scan was predictive of subsequent relapse rate over the study duration and also correlated with the subsequent enhancing lesion activity over the study period. A significant correlation was found between change in lesion load and disease activity on the monthly scans. Our results suggest that annual lesion load quantification provides an efficient measure of ongoing disease activity, and this supports its application as a surrogate marker of disease evolution in phase III treatment trials.

Serum angiotensin-converting enzyme in multiple sclerosis.

To determine the extent and significance of serum angiotensin-converting enzyme (ACE) elevation in multiple sclerosis (MS) and the correlation between serum ACE activity and clinical and magnetic resonance imaging (MRI) indicators of disease activity. A retrospective cross-sectional study of 45 consecutive patients with clinically definite MS and a longitudinal study of 30 additional patients with clinically definite MS involved in a long-term study of neurologic function and MRI in MS. Comprehensive MS center of a tertiary care university hospital. A total of 75 patients with clinically definite MS and 31 healthy controls. Serum ACE activity was measured using a spectrophotometric assay and correlated with clinical indicators of disease activity and with total cerebral MS lesion volume measured by MRI. An elevated ACE activity was found in 17 (23%) of 75 patients with MS as compared with 2 (6%) of 31 healthy controls. Changes in serum ACE activity correlated with changes in total plaque volume on MRI. Serum ACE activity may be an indicator of disease activity in longitudinal analysis. Also, elevated ACE activity in a patient with otherwise typical MS need not raise suspicions of alternative diagnoses.

Multiple sclerosis lesion quantification using fuzzy-connectedness principles.

Multiple sclerosis (MS) is a disease of the white matter. Magnetic resonance imaging (MRI) is proven to be a sensitive method of monitoring the progression of this disease and of its changes due to treatment protocols. Quantification of the severity of the disease through estimation of MS lesion volume via MR imaging is vital for understanding and monitoring the disease and its treatment. This paper presents a novel methodology and a system that can be routinely used for segmenting and estimating the volume of MS lesions via dual-echo fast spin-echo MR imagery. A recently developed concept of fuzzy objects forms the basis of this methodology. An operator indicates a few points in the images by pointing to the white matter, the grey matter, and the cerebro-spinal fluid (CSF). Each of these objects is then detected as a fuzzy connected set. The holes in the union of these objects correspond to potential lesion sites which are utilized to detect each potential lesion as a three-dimensional (3-D) fuzzy connected object. These objects are presented to the operator who indicates acceptance/rejection through the click of a mouse button. The number and volume of accepted lesions is then computed and output. Based on several evaluation studies, we conclude that the methodology is highly reliable and consistent, with a coefficient of variation (due to subjective operator actions) of 0.9% (based on 20 patient studies, three operators, and two trials) for volume and a mean false-negative volume fraction of 1.3%, with a 95% confidence interval of 0%-2.8% (based on ten patient studies).