Learning multi-site harmonization of magnetic resonance images without traveling human phantoms.

ABSTRACT: Harmonization improves Magn. Reson. Imaging (MRI) data consistency and is central to effective integration of diverse imaging data acquired across multiple sites. Recent deep learning techniques for harmonization are predominantly supervised in nature and hence require imaging data of the same human subjects to be acquired at multiple sites. Data collection as such requires the human subjects to travel across sites and is hence challenging, costly, and impractical, more so when sufficient sample size is needed for reliable network training. Here we show how harmonization can be achieved with a deep neural network that does not rely on traveling human phantom data. Our method disentangles site-specific appearance information and site-invariant anatomical information from images acquired at

SUBMITTER: Liu S 

PROVIDER: S-EPMC10898625 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature