Random forest regression for magnetic resonance image synthesis

Amod Jog, Aaron Carass, Snehashis Roy, Dzung L. Pham, Jerry L. Prince

Abstract: Highlights•We describe an MRI image synthesis algorithm capable of synthesizing full-head T2w images and FLAIR images.•Our algorithm, REPLICA, is a supervised method and learns the nonlinear intensity mappings for synthesis using innovative features and a multi-resolution design.•We show significant improvement in synthetic image quality over state-of-the-art image synthesis algorithms.•We also demonstrate that image analysis tasks like segmentation perform similarly for real and REPLICA-generated synthetic images.•REPLICA is computationally very fast and can be easily used as a preprocessing tool before further image analysis.