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International Journal of Biomedical Imaging
Volume 2008 (2008), Article ID 341684, 12 pages
Research Article

Reordering for Improved Constrained Reconstruction from Undersampled k-Space Data

1Laboratory for Structural NMR Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
2Electrical and Computer Engineering Department, University of Utah, Salt Lake City, UT 84112, USA
3Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, UT 84108, USA
4Department of Bioengineering, University of Utah, Salt Lake City, UT 84112, USA

Received 8 April 2008; Revised 29 June 2008; Accepted 2 October 2008

Academic Editor: Habib Zaidi

Copyright © 2008 Ganesh Adluru and Edward V. R. DiBella. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently, there has been a significant interest in applying reconstruction techniques, like constrained reconstruction or compressed sampling methods, to undersampled k-space data in MRI. Here, we propose a novel reordering technique to improve these types of reconstruction methods. In this technique, the intensities of the signal estimate are reordered according to a preprocessing step when applying the constraints on the estimated solution within the iterative reconstruction. The ordering of the intensities is such that it makes the original artifact-free signal monotonic and thus minimizes the finite differences norm if the correct image is estimated; this ordering can be estimated based on the undersampled measured data. Theory and example applications of the method for accelerating myocardial perfusion imaging with respiratory motion and brain diffusion tensor imaging are presented.