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.
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