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International Journal of Biomedical Imaging
Volume 2017, Article ID 7835749, 11 pages
Research Article

Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast

1School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
2Department of Mathematics, Vanderbilt University, Nashville, TN, USA
3Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
4Institute for Computational and Engineering Sciences and Departments of Biomedical Engineering and Internal Medicine, The University of Texas at Austin, Austin, TX, USA
5Department of Mathematics, Nanjing University, Nanjing, Jiangsu, China

Correspondence should be addressed to Dong Wang; nc.ude.tsujn@352211113

Received 24 March 2017; Accepted 20 July 2017; Published 28 August 2017

Academic Editor: Guowei Wei

Copyright © 2017 Dong Wang et al. 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.


Purpose. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled -space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast. Methods. We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters (volume transfer constant) and (extravascular-extracellular volume fraction) across a population of random sampling schemes. Results. NN produced the lowest image error (SER: 29.1), while produced the most accurate (CCC: 0.974/0.974) and (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate (CCC: 0.842) and (CCC: 0.799). Conclusion. should be used as temporal constraints for CS DCE-MRI of the breast.