ArticleNeuroradiology2026
Deep learning-accelerated 3D flair for white matter lesion detection in multiple sclerosis: a feasibility study.
Article in Neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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11 authors.
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Abstract
objectivesDeep learning (DL)-based image reconstruction (DLBIR) techniques promise accelerated MRI acquisitions with enhanced image quality. Herein, we compare the image quality of a DLBIR-based 3D FLAIR (3D-FLAIR MATERIALS AND
methodsOur prospective, reader-blinded study, included 26 MS patients who underwent both sequences on a 3T scanner during the same study session over three months. Two neuroradiologists assessed noise, artifacts, sharpness, overall image quality, and diagnostic confidence using 4-point Likert-like scales. Lesion conspicuity was graded for lesions < 3 mm and ≥ 3 mm. Quantitative metrics included lesion count, apparent signal-to-noise ratio (aSNR), and contrast-to-noise ratio (aCNR). A composite gold standard was used to calculate sensitivity and precision.
results3D-FLAIR
conclusionDLBIR 3D FLAIR significantly improves lesion detection and image quality in MS, supporting its potential integration into standard imaging protocols. As DLBIR algorithms evolve, further validation in larger, diverse cohorts will be essential.
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