Abstract:
In high-angular-resolution diffusion imaging (HARDI), simultaneous multislice (SMS) acquisition incorporated in multi-coil parallel imaging offers speedups in addition to...Show MoreMetadata
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.
Date of Conference: 22-25 September 2019
Date Added to IEEE Xplore: 26 August 2019
ISBN Information: