A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks.

ABSTRACT: In structural magnetic resonance (MR) imaging, motion artefacts, low resolution, imaging noise and variability in acquisition protocols frequently degrade image quality and confound downstream analyses. Here we report a foundation model for the motion correction, resolution enhancement, denoising and harmonization of MR images. Specifically, we trained a tissue-classification neural network to predict tissue labels, which are then leveraged by a 'tissue-aware' enhancement network to generate high-quality MR images. We validated the model's effectiveness on a large and diverse dataset comprising 2,448 deliberately corrupted images and 10,963 images spanning a wide age range (from foetuses to elderly individuals) acquired using a variety of clinical scanners across 19 public datasets. The mo

SUBMITTER: Sun Y 

PROVIDER: S-EPMC12360180 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature