Meeting Banner
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.

Keywords

able accelerate addition additionally among another arbitrary assuming augmented available avoid back biased biomedical central certainly choose comes components compressive computational computer consistency consists constrained contains convergence convex corrected corresponds cost criterion decomposition decrease denotes described detail diffusion dimension done efficient efficiently enforce engineering equation equivalent error errors every example finding fine fold formulated free generated global gold guess hand implement improve improves inclusion indicates introduce introduced iterations local long loop magnitude mainly many mathematics matrix model models moreover nearly necessary negligible negligibly norm note nuclear optimization original part pattern plot practically principal prior priors problem proposed quality radiology rank reconstructed reconstruction reduced reducing regularization regularizes represented residual respect respectively sampled scale scaled science sciences self serves several similarity simple since singular solution solve solved solving space sparsity step steps stopping straightforward subject submitted suitable technology tedious term terms thresholding transform tuned tuning uncorrected unlike update utilize variation various wavelet zoom