Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.

ABSTRACT:

Objective

To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reconstructed.

Materials and methods

Twelve-fold accelerated 3D T2-FLAIR images were obtained from a cohort of 62 patients with neurological deficits on 3 T MRI. Images were reconstructed offline via CS and the CIRIM. Image quality was assessed in a blinded and randomized manner by two experienced interventional neuroradiologists and one experienced pediatric neuroradiologist on imaging artifacts, perceived spatial resolution (sharpness), anatomic conspicuity, diagnostic confidence, and contrast. The methods were also compared in term

SUBMITTER: Liebrand LC 

PROVIDER: S-EPMC11790796 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature