| Compressed sensing reconstruction for magnetic resonance parameter mapping. | |
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MedLine Citation:
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PMID: 20564599 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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Compressed sensing (CS) holds considerable promise to accelerate the data acquisition in magnetic resonance imaging by exploiting signal sparsity. Prior knowledge about the signal can be exploited in some applications to choose an appropriate sparsifying transform. This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal. The approach is presented and evaluated in simulations and in in vivo T(1) and T(2) mapping experiments in the brain. Accurate T(1) and T(2) maps are obtained from highly reduced data. This model-based reconstruction could also be applied to other MR parameter mapping applications like diffusion and perfusion imaging. |
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Authors:
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Mariya Doneva; Peter Börnert; Holger Eggers; Christian Stehning; Julien Sénégas; Alfred Mertins |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine Volume: 64 ISSN: 1522-2594 ISO Abbreviation: Magn Reson Med Publication Date: 2010 Oct |
Date Detail:
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Created Date: 2010-09-27 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8505245 Medline TA: Magn Reson Med Country: United States |
Other Details:
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Languages: eng Pagination: 1114-20 Citation Subset: IM |
Affiliation:
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Institute for Signal Processing, University of Luebeck, Luebeck, Germany. mariya.doneva@isip.uni-luebeck.de |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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