We develop a novel harmonization approach for T1‐weighted magnetic resonance imaging using a style‐encoding generative adversarial network that can be used to harmonize entire images for a variety of international, multi‐cohort, neuroimaging collaborations. Results demonstrated that this model avoids the need to control for clinical or demographic information. We showed that our harmonization removed the cross‐site variances, while preserving the anatomical information and clinical meaningful patterns.
All Keywords
【저자키워드】 MRI, GAN, harmonization, style‐transfer,
【저자키워드】 MRI, GAN, harmonization, style‐transfer,