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Proceedings Paper

Iterative wavelet thresholding for rapid MRI reconstruction
Author(s): Mohammad H. Kayvanrad; Charles A. McKenzie; Terry M. Peters
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Paper Abstract

According to the developments in the field of compressed sampling and and sparse recovery, one might take advantage of the sparsity of an object, as an additional a priori knowledge about the object, to reconstruct it from fewer samples than that needed by the traditional sampling strategies. Since most magnetic resonance (MR) images are sparse in some domain, in this work we consider the problem of MR reconstruction and how one could apply this idea to accelerate the process of MR image/map acquisition. In particular, based on the Paupolis-Gerchgerg algorithm, an iterative thresholding algorithm for reconstruction of MR images from limited k-space observations is proposed. The proposed method takes advantage of the sparsity of most MR images in the wavelet domain. Initializing with a minimum-energy reconstruction, the object of interest is reconstructed by going through a sequence of thresholding and recovery iterations. Furthermore, MR studies often involve acquisition of multiple images in time that are highly correlated. This correlation can be used as additional knowledge on the object beside the sparsity to further reduce the reconstruction time. The performance of the proposed algorithms is experimentally evaluated and compared to other state-of-the-art methods. In particular, we show that the quality of reconstruction is increased compared to total variation (TV) regularization, and the conventional Papoulis-Gerchberg algorithm both in the absence and in the presence of noise. Also, phantom experiments show good accuracy in the reconstruction of relaxation maps from a set of highly undersampled k-space observations.

Paper Details

Date Published: 15 March 2011
PDF: 10 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79624S (15 March 2011); doi: 10.1117/12.878347
Show Author Affiliations
Mohammad H. Kayvanrad, The Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)
Charles A. McKenzie, The Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)
Terry M. Peters, The Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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