Physics-informed deep learning reconstruction for ultrafast clinical 3D fluid-attenuated inversion recovery brain MRI.

ABSTRACT:

Background

Physics-informed deep learning (DL) reconstructions show promise in accelerating MRI yet have not been extensively validated, particularly for 3D fluid-attenuated inversion recovery (FLAIR) sequence.

Purpose

To evaluate the diagnostic quality and interchangeability of DL-based 3D FLAIR with a state-of-the-art acceleration technique (wave-controlled aliasing in parallel imaging [Wave-CAIPI] FLAIR) in a clinical setting with 3 T brain MRI.

Materials and methods

Participants undergoing evaluation for demyelinating disease between October and December of 2023 were prospectively enrolled at a single center. For each participant, state-of-the-art Wave-CAIPI FLAIR and a resolution-matched 6-fold-under-sampled Cartesian FLAIR acquisition with DL reconstruction were

SUBMITTER: Fujita S 

PROVIDER: S-EPMC12429252 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature