Model-Based Reconstruction of Undersampled DTI Data

Christopher L. Welsh^1,2, Edward W. Hsu^1,2, Edward V. R. DiBella^1,2

Diffusion Tensor Imaging (DTI) is useful for characterizing tissue microstructure, but suffers from long scan time and low SNR. To allow faster acquisition, a model-based strategy is presented to directly estimate diffusion tensors from undersampled k-space data. Using an acceleration factor of 2, different sampling schemes were investigated and found to generally outperform acquiring equivalent number of full-resolution scans. Minor performance differences were also observed among the schemes for estimating different DTI parameters. These findings suggest the proposed strategy can be used to reduce DTI scan time by half while incurring little or no loss in the parameter estimation accuracy.