scholarly journals Multiple sclerosis lesion enhancement and white matter region estimation using hyperintensities in FLAIR images

2019 ◽  
Vol 49 ◽  
pp. 338-348 ◽  
Author(s):  
Paulo G.L. Freire ◽  
Ricardo J. Ferrari
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Multiple Sclerosis Lesion ◽  
White Matter Region

scholarly journals A R-Script for Generating Multiple Sclerosis Lesion Pattern Discrimination Plots

2021 ◽  
Vol 11 (1) ◽  
pp. 90
Author(s):  
Robert Marschallinger ◽  
Carmen Tur ◽  
Hannes Marschallinger ◽  
Johann Sellner
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Demyelinating Disease ◽  
White Matter Lesions ◽  
Pattern Discrimination ◽  
Small Sample ◽  
Multiple Sclerosis Lesion ◽  
Normal Brain ◽  
Data Set ◽  
Lesion Pattern

One significant characteristic of Multiple Sclerosis (MS), a chronic inflammatory demyelinating disease of the central nervous system, is the evolution of highly variable patterns of white matter lesions. Based on geostatistical metrics, the MS-Lesion Pattern Discrimination Plot reduces complex three- and four-dimensional configurations of MS-White Matter Lesions to a well-arranged and standardized two-dimensional plot that facilitates follow-up, cross-sectional and medication impact analysis. Here, we present a script that generates the MS-Lesion Pattern Discrimination Plot, using the widespread statistical computing environment R. Input data to the script are Nifti-1 or Analyze-7.5 files with individual MS-White Matter Lesion masks in Montreal Normal Brain geometry. The MS-Lesion Pattern Discrimination Plot, variogram plots and associated fitting statistics are output to the R console and exported to standard graphics and text files. Besides reviewing relevant geostatistical basics and commenting on implementation details for smooth customization and extension, the paper guides through generating MS-Lesion Pattern Discrimination Plots using publicly available synthetic MS-Lesion patterns. The paper is accompanied by the R script LDPgenerator.r, a small sample data set and associated graphics for comparison.

scholarly journals Assessment of Artificial Intelligence Automatic Multiple Sclerosis Lesion Delineation Tool for Clinical Use

2021 ◽  
Author(s):  
Amalie Monberg Hindsholm ◽  
Stig Præstekjær Cramer ◽  
Helle Juhl Simonsen ◽  
Jette Lautrup Frederiksen ◽  
Flemming Andersen ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Quantitative Evaluation ◽  
White Matter Lesions ◽  
Qualitative Evaluation ◽  
White Matter Hyperintensity ◽  
Multiple Sclerosis Lesion ◽  
Dice Similarity Coefficient ◽  
Accuracy Score ◽  
Clinical Value

Abstract Purpose To implement and validate an existing algorithm for automatic delineation of white matter lesions on magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) on a local single-center dataset. Methods We implemented a white matter hyperintensity segmentation model, based on a 2D convolutional neural network, using the conventional T2-weighted fluid attenuated inversion recovery (FLAIR) MRI sequence as input. The model was adapted for delineation of MS lesions by further training on a local dataset of 93 MS patients with a total of 3040 lesions. A quantitative evaluation was performed on ten test patients, in which model-generated masks were compared to manually delineated masks from two expert delineators. A subsequent qualitative evaluation of the implemented model was performed by two expert delineators, in which generated delineation masks on a clinical dataset of 53 patients were rated acceptable (< 10% errors) or unacceptable (> 10% errors) based on the total number of true lesions. Results The quantitative evaluation resulted in an average accuracy score (F1) of 0.71, recall of 0.77 and dice similarity coefficient of 0.62. Our implemented model obtained the highest scores in all three metrics, when compared to three out of the box lesion segmentation models. In the clinical evaluation an average of 94% of our 53 model-generated masks were rated acceptable. Conclusion After adaptation to our local dataset, the implemented segmentation model was able to delineate MS lesions with a high clinical value as rated by delineation experts while outperforming popular out of the box applications. This serves as a promising step towards implementation of automatic lesion delineation in our MS clinic.

Author response for "White matter lesions in multiple sclerosis are enriched for CD20 dim CD8 + tissue‐resident memory T cells"

2020 ◽  
Author(s):  
Cheng‐Chih Hsiao ◽  
Nina L. Fransen ◽  
Aletta M.R. den Bosch ◽  
Kim I.M. Brandwijk ◽  
Inge Huitinga ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
T Cells ◽  
White Matter ◽  
Memory T Cells ◽  
White Matter Lesions ◽  
Author Response ◽  
Resident Memory T Cells

scholarly journals Bi-caudate ratio as a MRI marker of white matter atrophy in multiple sclerosis and ischemic leukocencephalopathy

2019 ◽  
Vol 50 (1) ◽  
Author(s):  
Dena Serag ◽  
Eman Ragab
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Surrogate Marker ◽  
Normal Subjects ◽  
Subjective Assessment ◽  
Linear Measurement ◽  
Control Group ◽  
Imaging Science ◽  
The Difference ◽  
Sensitivity Specificity

Abstract Background Brain atrophy measurement is now a cornerstone in basic neuro-imaging science. While assessment of white matter atrophy by visual inspection is subjective, volumetric approaches are time-consuming and not often feasible. Bi-caudate ratio represents a linear surrogate parameter of brain volume that can be derived from standard imaging sequences. This study highlights the value of the bi-caudate ratio (BCR) as a MRI marker of white matter atrophy in patients with multiple sclerosis and ischemic leukoencephalopathy and set a cut-off value to differentiate between patients with white matter atrophy and normal subjects. Results A total of 115 patients (54 males and 61 females) diagnosed with white matter leukoencephalopathy (MS in 51 patients and ischemic leukoencephalopathy in 64 patients) were included. Another group of 60 subjects with a normal white matter signal was recruited as a control group. BCR for the patient group ranged from 0.13 to 0.27 (mean (± SD) = 0.16 ± 0.02), while for the control group, it ranged from 0.05 mm to 0.13 (mean (± SD) = 0.09 ± 0.01). The difference between the two groups was statistically significant (P value < 0.001). A cut-off value of 0.13 was used to differentiate between the BCR in both patients and control groups with sensitivity, specificity, and accuracy of 99.2%, 100%, and 99%, respectively. The difference in BCR for patients diagnosed with MS and ischemic leukoencephalopathy was also statistically significant (P value < 0.001). Conclusion The bi-caudate ratio represents a linear measurement of subcortical atrophy that can be useful as a surrogate marker of global supra-tentorial white matter atrophy instead of the usually performed visual and therefore subjective assessment. It is an easily obtained measure that can be performed without complex time-consuming volumetric studies. Our findings also revealed that the BCR is higher in patients with ischemic leukoencephalopathy than in patients with MS.

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