A network-assisted joint image and motion estimation approach for robust 3D MRI motion correction across severity levels.

ABSTRACT:

Purpose

The purpose of this work was to develop and evaluate a novel method that leverages neural networks and physical modeling for 3D motion correction at different levels of corruption.

Methods

The novel method ("UNet+JE") combines an existing neural network ("UNet_mag") with a physics-informed algorithm for jointly estimating motion parameters and the motion-compensated image ("JE"). UNet_mag and UNet+JE were trained on two training datasets separately with different distributions of motion corruption severity and compared to JE as a benchmark. All five resulting methods were tested on T₁w 3D MPRAGE scans of healthy participants with simulated (n = 40) and in vivo (n = 10) motion corruption ranging from mild to severe motion.

Results

SUBMITTER: Nghiem B 

PROVIDER: S-EPMC12620172 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature