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Updated: Oct 17, 2025
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
Fully automated detection of paramagnetic rims in multiple sclerosis lesions on 3T susceptibility-based MR imaging
Carolyn Lou1, Pascal Sati2, Martina Absinta3
1Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Background And Purpose:
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.
Methods:
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.
Results:
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]).
Conclusion:
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.
Insights
We developed APRL, an automated method to detect paramagnetic rim lesions in multiple sclerosis (MS) using MRI. This technique accurately identifies these lesions, which are linked to more severe MS disease courses.
Area of Science:
- Radiology
- Neuroimaging
- Artificial Intelligence
Background:
- Paramagnetic rim lesions are a pathological hallmark of a specific type of multiple sclerosis (MS) lesion.
- Increased prevalence of these lesions correlates with a more severe MS disease course.
- Manual identification of these lesions is time-consuming and labor-intensive.
Purpose of the Study:
- To develop and validate an automated method for detecting paramagnetic rim lesions on 3T MRI.
- To present the Automated Paramagnetic Rim Lesion (APRL) detection technique.
Main Methods:
- Utilized T1-weighted, T2-FLAIR, and T2*-phase brain MRI from 20 MS subjects.
- Employed automated lesion segmentation, center detection, labeling, and radiomic feature extraction.
- Trained a random forest classification model on 753 lesions from 16 patients and tested on 198 lesions from 4 patients.
Main Results:
- The automated method showed high correlation (r=0.86) with gold standard counts for paramagnetic rim lesions per subject.
- The classification algorithm achieved an area under the curve of 0.82 for detecting these lesions.
Conclusions:
- The study successfully developed APRL, a fully automated technique for detecting paramagnetic rim lesions.
- APRL utilizes standard 3T MRI sequences (T1, FLAIR, T2*), making it potentially widely applicable.
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