Fully automated detection of paramagnetic rims in multiple sclerosis lesions on 3T susceptibility-based MR imaging

•Paramagnetic rim lesions are an important subtype of multiple sclerosis lesion.•Automated methods can accelerate the assessment of paramagnetic rim lesions.•APRL automatically identifies and accurately classifies paramagnetic rim lesions. The presence of a paramagnetic rim around a white matter les...

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Published in:NeuroImage clinical Vol. 32; p. 102796
Main Authors: Lou, Carolyn, Sati, Pascal, Absinta, Martina, Clark, Kelly, Dworkin, Jordan D., Valcarcel, Alessandra M., Schindler, Matthew K., Reich, Daniel S., Sweeney, Elizabeth M., Shinohara, Russell T.
Format: Journal Article
Language:English
Published: Netherlands Elsevier Inc 01.01.2021
Elsevier
Subjects:
Brain - diagnostic imaging
Disease Progression
Humans
Magnetic Resonance Imaging
Multiple sclerosis
Multiple Sclerosis - diagnostic imaging
Neuroimaging
Paramagnetic rim lesions
Radiology
Regular
White Matter - diagnostic imaging
Neuroimaging
Multiple sclerosis
PRL
Paramagnetic rim lesions
paramagnetic rim lesion
ISSN:2213-1582, 2213-1582
Online Access:Get full text
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Summary:•Paramagnetic rim lesions are an important subtype of multiple sclerosis lesion.•Automated methods can accelerate the assessment of paramagnetic rim lesions.•APRL automatically identifies and accurately classifies paramagnetic rim lesions. The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis lesion. Increased prevalence of these paramagnetic rim lesions is associated with a more severe disease course in MS, but manual identification is time-consuming. We present APRL, a method to automatically detect paramagnetic rim lesions on 3T T2*-phase images. T1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 20 subjects with MS. The images were then processed with automated lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 951 lesions were identified, 113 (12%) of which contained a paramagnetic rim. We divided our data into a training set (16 patients, 753 lesions) and a testing set (4 patients, 198 lesions), fit a random forest classification model on the training set, and assessed our ability to classify paramagnetic rim lesions on the test set. The number of paramagnetic rim lesions per subject identified via our automated lesion labelling method was highly correlated with the gold standard count per subject, r = 0.86 (95% CI [0.68, 0.94]). The classification algorithm using radiomic features classified lesions with an area under the curve of 0.82 (95% CI [0.74, 0.92]). This study develops a fully automated technique, APRL, for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.
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Both authors contributed equally to this work.
ISSN:2213-1582
2213-1582
DOI:10.1016/j.nicl.2021.102796