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Proceedings Paper

Motion-compensated compressed sensing for dynamic imaging
Author(s): Rajagopalan Sundaresan; Yookyung Kim; Mariappan S. Nadar; Ali Bilgin
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Paper Abstract

The recently introduced Compressed Sensing (CS) theory explains how sparse or compressible signals can be reconstructed from far fewer samples than what was previously believed possible. The CS theory has attracted significant attention for applications such as Magnetic Resonance Imaging (MRI) where long acquisition times have been problematic. This is especially true for dynamic MRI applications where high spatio-temporal resolution is needed. For example, in cardiac cine MRI, it is desirable to acquire the whole cardiac volume within a single breath-hold in order to avoid artifacts due to respiratory motion. Conventional MRI techniques do not allow reconstruction of high resolution image sequences from such limited amount of data. Vaswani et al. recently proposed an extension of the CS framework to problems with partially known support (i.e. sparsity pattern). In their work, the problem of recursive reconstruction of time sequences of sparse signals was considered. Under the assumption that the support of the signal changes slowly over time, they proposed using the support of the previous frame as the "known" part of the support for the current frame. While this approach works well for image sequences with little or no motion, motion causes significant change in support between adjacent frames. In this paper, we illustrate how motion estimation and compensation techniques can be used to reconstruct more accurate estimates of support for image sequences with substantial motion (such as cardiac MRI). Experimental results using phantoms as well as real MRI data sets illustrate the improved performance of the proposed technique.

Paper Details

Date Published: 7 September 2010
PDF: 8 pages
Proc. SPIE 7798, Applications of Digital Image Processing XXXIII, 77980A (7 September 2010); doi: 10.1117/12.861113
Show Author Affiliations
Rajagopalan Sundaresan, The Univ. of Arizona (United States)
Yookyung Kim, The Univ. of Arizona (United States)
Mariappan S. Nadar, Siemens Corporate Research (United States)
Ali Bilgin, The Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 7798:
Applications of Digital Image Processing XXXIII
Andrew G. Tescher, Editor(s)

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