An image-domain deep-learning denoising technique for accelerated parallel brain MRI: prospective clinical evaluation.

ABSTRACT:

Background

Parallel imaging can accelerate MRI acquisitions, but excessive accelerations can introduce amplified noise and aliasing artifacts.

Purpose

To evaluate a vendor-agnostic AI-based approach to remove image degradation artifacts in highly accelerated MRI scans, improving image quality and reducing scan time.

Materials and methods

Training was performed by retrospectively degrading standard accelerated images. Evaluation was performed with both retrospective and prospectively-collected highly accelerated images. Retrospective data were taken from ∼2000 MRI studies obtained between August 2016 and October 2022, and prospective data were collected in >200 subjects between June and November 2022, using scanners from multiple vendors and locations. Scan time data w

SUBMITTER: Onac L 

PROVIDER: S-EPMC12481681 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature