By Topic

Compass: a joint framework for Parallel Imaging and Compressive Sensing in MRI

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)

Parallel Imaging MRI (pMRI) and Compressive Sensing (CS) are two reconstruction techniques that have recently been applied to increase MRI performance. In this paper we demonstrate that a combined analysis of the pMRI and CS problems leads to a conceptually simple, yet effective technique that outperforms independent approaches to both reconstruction problems. We argue that the proposed technique is also naturally resilient to noise, due to its relation to the MAP image denoising formulation. A modified Basis Pursuit (BP) formulation of the CS-MRI problem allows it to handle the pMRI problem at the same time. We also present an exact solution to this BP problem, using the split Bregman technique, with discrete shearlet transform (DST) regularization. The DST is an excellent choice for natural image applications, due to its optimal sparsity property. Results show that this Compressive Parallel Sensing (COMPASS) reconstruction algorithm outperforms more traditional MRI reconstruction algorithms in both pMRI and CS experiments.

Published in:

Image Processing (ICIP), 2010 17th IEEE International Conference on

Date of Conference:

26-29 Sept. 2010