Random Forests for Simultaneous-Multislice (SMS) Undersampled HARDI Reconstruction and Uncertainty Estimation

Abstract

In high-angular-resolution diffusion imaging (HARDI), simultaneous multislice (SMS) acquisition incorporated in multi-coil parallel imaging offers speedups in addition to the speedup obtained from undersampling gradient directions. We propose a novel learning-based method for reconstructing direction-undersampled SMS HARDI data. Our method relies on random-forest regression that also informs on the uncertainty in the reconstructions stemming from noise and artifacts. Results on a large clinical HARDI dataset show that our method significantly improves over the state of the art on SMS HARDI reconstruction qualitatively and quantitatively.