Abstract #2605

Compressive Diffusion MRI Part 3: Prior-Image Constrained Low-Rank Model (PCLR)

Hao Gao¹,², Longchuan Li³, Xiaoping P. Hu³

¹Department of Mathematics and Computer Science, Emory University, Atlanta, GA, United States; ²Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States; ³Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States

In another submitted abstract Compressive Diffusion MRI Part 1: Why Low-Rank?, we compared several sparsity models and found that the low-rank (LR) model is the most suitable for diffusion MRI. This abstract introduces the Prior-image Constrained LR (PCLR) model, through which prior images can be efficiently incorporated to improve LR. In addition, a simple-to-implement and efficient algorithm has been developed to solve PCLR. The application of PCLR to diffusion MRI, with the prior images that are different from the images to be reconstructed, showed that PCLR performs better than LR.