ArticleProceedings. IEEE International Symposium on Biomedical Imaging2025
MEASURING IMPACT OF SUPER-RESOLUTION ON SPINAL CORD MRI SCANS: LESION DETECTION SENSITIVITY, VARIABILITY, AND CLINICAL IMPACT.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
The efficacy and potential for MRI-derived spinal cord (SC) information is an area of great interest for the study of multiple sclerosis (MS). Though the presence of SC lesions aid in the diagnosis of MS or other disease/injury, there is much debate as to whether lesions can help to predict symptoms or disability. The correlation between spinal cord lesions and MS disability is weak, even when observed at a higher resolution. A current drawback in the collection of SC magnetic resonance imaging (MRI) scans is the inability to collect high-resolution images both through-plane and in-plane. Super-resolution offers the opportunity to transform these anisotropic MRIs into high-resolution, isotropic images offering a view not previously possible. Here, we investigate how artificially altering the resolution of SC MRIs (either through super-resolution or linear interpolation) might enhance our ability to discern clinically relevant structures, including lesion load and its relation to several clinical variables, such as EDSS, across 53 patients with varying MS severity. Artificially altering the MRIs to varying levels of isotropic resolution increased sensitivity for lesion segmentation using open-source deep learning tools, but no significant association between lesion load/volume and EDSS disability measurement was found.
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Registered trials
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