AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts.

ABSTRACT:

Objectives

Artifacts in clinical MRI can compromise the performance of AI models. This study evaluates how different data augmentation strategies affect an AI model's segmentation performance under variable artifact severity.

Materials and methods

We used an AI model based on the nnU-Net architecture to automatically quantify lower limb alignment using axial T2-weighted MR images. Three versions of the AI model were trained with different augmentation strategies: (1) no augmentation ("baseline"), (2) standard nnU-net augmentations ("default"), and (3) "default" plus augmentations that emulate MR artifacts ("MRI-specific"). Model performance was tested on 600 MR image stacks (right and left; hip, knee, and ankle) from 20 healthy participants (mean age, 23 ± 3 years, 17 men),

SUBMITTER: Westfechtel SD 

PROVIDER: S-EPMC12559149 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature