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Title:The Study of Efficient Sparse Signal Reconstruction Algorithms for Compressive Sensing and the Application for Magnetic Resonance Imaging
Author(s):Gunawan, Aldi Indra; Lee, Kiryung
Contributor(s):Bresler, Yoram
Subject(s):magnetic resonance imaging
signal reconstruction
sparse signal reconstruction
compressed sensing algorithms
Abstract:Compressed sensing (CS) is a recently developed scheme in the signal processing that enables the reconstruction of sparse signals from limited number of measurements. This technique can significantly reduce the time required to scan an object in magnetic resonance imaging (MRI). The goal of this project is to study the feasibility and effectiveness of an efficient class of CS algorithms to MRI. Currently available implementations of state-of-the-art reconstruction algorithms for CS, such as regularized orthogonal matching pursuit, compressive sampling matching pursuit and subspace pursuit, do not scale well enough with the problem size to allow their practical application to MRI. The first part of this project, therefore, involves the development of computationally efficient implementations of these algorithms. The second part of the project involves a comparison of the optimized algorithms in terms of the tradeoff they offer between reconstructed image quality and computation for a range of reduced measurement scenarios in actual magnetic resonance image.
Issue Date:2008-12
Publication Status:unpublished
Peer Reviewed:not peer reviewed
Date Available in IDEALS:2014-03-12

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