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Abstract #5028

Enhanced Spinal Cord Lesion Detection in Multiple Sclerosis Using White-Matter-Nulled 3D MPRAGE with Deep Learning Reconstruction

Fanny Munsch1, Amaury Ravache2, Takayuki Yamamoto3,4, Bei Zhang5, Marion Lacoste2, Hikaru Fukutomi6, Thomas Tourdias1,2,7, and Vincent Dousset1,2,7
1Institute of Bioimaging, University of Bordeaux, Bordeaux, France, 2Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, Bordeaux, France, 3Kyoto University Hospital, Kyoto, Japan, 4Kyoto University, Kyoto, Japan, 5Canon Medical Systems Europe, Zoetermeer, Netherlands, 6Kansai Electric Power Hospital, Osaka, Japan, 7INSERM, Neurocentre Magendie, U1215, University of Bordeaux, Bordeaux, France

Synopsis

Keywords: Multiple Sclerosis, Spinal Cord, Multiple Sclerosis, Spinal cord imaging, 3d White-Matter-Nulled MPRAGE

Motivation: Multiple sclerosis commonly presents spinal cord lesions, which can lead to disabling symptoms. Conventional MRI sequences often lack sensitivity for their detection.

Goal(s): This study aimed to evaluate whether 3D WMn MPRAGE imaging combined with deep learning reconstruction could improve lesion visibility, detection accuracy, and confidence among neuroradiologists.

Approach: Thirty-eight patients with RRMS or CIS underwent MRI scans, including conventional and 3D WMn MPRAGE sequences. Lesion detection, contrast-to-noise ratios, and artifact levels were assessed by four neuroradiologists.

Results: The 3D WMn MPRAGE sequence significantly outperformed conventional sequences in lesion detection indicating its potential to improve MS spinal cord imaging.

Impact: This study demonstrates that 3D WMn MPRAGE combined with deep learning reconstruction significantly improves spinal cord lesion detection in multiple sclerosis, offering superior contrast and fewer artifacts than conventional MRI, potentially enhancing early diagnosis and treatment outcomes.

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Keywords