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

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 k-space data using nonlinear recovery schemes. The purpose of this pa...

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Main Authors: Dong Wang, Lori R. Arlinghaus, Thomas E. Yankeelov, Xiaoping Yang, David S. Smith
Format: Article
Language:English
Published: Wiley 2017-01-01
Series:International Journal of Biomedical Imaging
Online Access:http://dx.doi.org/10.1155/2017/7835749
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author Dong Wang
Lori R. Arlinghaus
Thomas E. Yankeelov
Xiaoping Yang
David S. Smith
author_facet Dong Wang
Lori R. Arlinghaus
Thomas E. Yankeelov
Xiaoping Yang
David S. Smith
author_sort Dong Wang
collection DOAJ
description 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 k-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 (TGVα2), 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 Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes. Results. NN produced the lowest image error (SER: 29.1), while TV/TGVα2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799). Conclusion. TV/TGVα2 should be used as temporal constraints for CS DCE-MRI of the breast.
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spelling doaj-art-175bb1cf1fab49ef9bca4059235751522025-08-20T02:04:54ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962017-01-01201710.1155/2017/78357497835749Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the BreastDong Wang0Lori R. Arlinghaus1Thomas E. Yankeelov2Xiaoping Yang3David S. Smith4School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu, ChinaVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USAInstitute for Computational and Engineering Sciences and Departments of Biomedical Engineering and Internal Medicine, The University of Texas at Austin, Austin, TX, USADepartment of Mathematics, Nanjing University, Nanjing, Jiangsu, ChinaVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USAPurpose. 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 k-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 (TGVα2), 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 Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes. Results. NN produced the lowest image error (SER: 29.1), while TV/TGVα2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799). Conclusion. TV/TGVα2 should be used as temporal constraints for CS DCE-MRI of the breast.http://dx.doi.org/10.1155/2017/7835749
spellingShingle Dong Wang
Lori R. Arlinghaus
Thomas E. Yankeelov
Xiaoping Yang
David S. Smith
Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast
International Journal of Biomedical Imaging
title Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast
title_full Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast
title_fullStr Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast
title_full_unstemmed Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast
title_short Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast
title_sort quantitative evaluation of temporal regularizers in compressed sensing dynamic contrast enhanced mri of the breast
url http://dx.doi.org/10.1155/2017/7835749
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