Accelerating Diffusion Tensor Imaging Using Multi-Reference Image Constrained Reconstruction

Lindsey Jean Healy¹, Osama Abdullah¹, Edward W. Hsu¹

MR diffusion tensor imaging (DTI) has suffered from long scan times, low SNR and resolution. This study examines the accuracy and efficiency of selected schemes to accelerate DTI via reduced encoding and multi-reference constrained reconstruction. Results indicate that generalized-series constrained reconstruction combined with direction-dependent, model-estimated images as references can be used to further improve DTI acquisition efficiency than that achieved previously using a single reference image. The findings support the validity of applying reduced sampling and constrained reconstruction to accelerate DTI, and are particularly promising for 3D DTI, high angular-resolution diffusion imaging, or other experiments that require extremely large datasets.