Parallel imaging reconstruction for arbitrary trajectories using k-space sparse matrices (kSPA)

Although the concept of receiving MR signal using multiple coils simultaneously has been known for over two decades, the technique has only recently become clinically available as a result of the development of several effective parallel imaging reconstruction algorithms. Despite the success of thes...

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Bibliographic Details
Published in:	Magnetic resonance in medicine Vol. 58; no. 6; pp. 1171 - 1181
Main Authors:	Liu, Chunlei, Bammer, Roland, Moseley, Michael E.
Format:	Journal Article
Language:	English
Published:	Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.12.2007
Subjects:	Algorithms Artificial Intelligence Brain - anatomy & histology functional imaging Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods kSPA magnetic resonance imaging Magnetic Resonance Imaging - instrumentation Magnetic Resonance Imaging - methods massive parallel imaging parallel imaging Pattern Recognition, Automated - methods Phantoms, Imaging Reproducibility of Results Sensitivity and Specificity sparse approximate inverse sparse matrix
ISSN:	0740-3194, 1522-2594
Online Access:	Get full text
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Summary:	Although the concept of receiving MR signal using multiple coils simultaneously has been known for over two decades, the technique has only recently become clinically available as a result of the development of several effective parallel imaging reconstruction algorithms. Despite the success of these algorithms, it remains a challenge in many applications to rapidly and reliably reconstruct an image from partially‐acquired general non‐Cartesian k‐space data. Such applications include, for example, three‐dimensional (3D) imaging, functional MRI (fMRI), perfusion‐weighted imaging, and diffusion tensor imaging (DTI), in which a large number of images have to be reconstructed. In this work, a systematic k‐space–based reconstruction algorithm based on k‐space sparse matrices (kSPA) is introduced. This algorithm formulates the image reconstruction problem as a system of sparse linear equations in k‐space. The inversion of this system of equations is achieved by computing a sparse approximate inverse matrix. The algorithm is demonstrated using both simulated and in vivo data, and the resulting image quality is comparable to that of the iterative sensitivity encoding (SENSE) algorithm. The kSPA algorithm is noniterative and the computed sparse approximate inverse can be applied repetitively to reconstruct all subsequent images. This algorithm, therefore, is particularly suitable for the aforementioned applications. Magn Reson Med, 2007. © 2007 Wiley‐Liss, Inc.
Bibliography:	National Institutes of Health (NIH) - No. 1K99NS057943-01; No. 1R01NS35959; No. 1R01EB002771 ark:/67375/WNG-X3GLLXG8-D Lucas Foundation Center of Advanced MR Technology of Stanford - No. NCRR P41 RR 09784 ArticleID:MRM21334 istex:6A7DA99D1501AA55CC344996CCFB6D1C4B155B7C ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Undefined-1 ObjectType-Feature-3
ISSN:	0740-3194 1522-2594
DOI:	10.1002/mrm.21334