Technical Program

Paper Detail

Paper:	SA-AM-OS1.3
Session:	MRI Acquisition and Reconstruction
Time:	Saturday, April 8, 10:10 - 10:30
Presentation:	Oral
Title:	Fast Regularized Reconstruction of Non-Uniformly Subsampled Parallel MRI Data
Authors:	W. Scott Hoge; Brigham and Women's Hospital / Harvard Medical School
	Misha E. Kilmer; Tufts University
	Steven J. Haker; Brigham and Women's Hospital / Harvard Medical School
	Dana H. Brooks; Northeastern University
	Walid E. Kyriakos; Brigham and Women's Hospital / Harvard Medical School
Abstract:	Parallel MR imaging is an effective approach to reduce MR image acquisition time. Non-uniform subsampling allows one to tailor the subsampling scheme for improved image quality at high acceleration factors. However, non-uniform subsampling precludes fast reconstruction schemes such as SENSE, and is more likely to require a regularized solution than reconstruction of uniformly subsampled data demands. This means that one needs to choose a good regularization parameter, typically requiring multiple expensive system solves. Here, we present an efficient LSQR-Hybrid algorithm which simultaneously addresses the need for rapid regularization parameter selection and fast reconstruction. This algorithm can reconstruct non-uniformly subsampled parallel MRI data, with automatic regularization and good image quality, in a time competitive with Cartesian SENSE.

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