Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.

ABSTRACT: Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51 × 0.45 mm³) and denoised super-resolution DL-VWI (0.28 × 0.28 × 0.45 mm³) of 117 patients were analyzed in this retrospective study. Quality of the images were compared qualitatively and quantitatively. Diagnostic performance for identifying potentially culprit atherosclerotic plaques, using lesion enhancement and presence of intraplaque hemorrhage (IPH), was evaluated. DL-VWI significantly outperformed Conv-VWI in all image quality ratings (all P < .001). DL-VWI demonstrated higher SNR and contrast-to-noise ratio (CNR) t

SUBMITTER: Seo M 

PROVIDER: S-EPMC11329665 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature