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Updated: Feb 22, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast
Dong Wang1,2, Lori R Arlinghaus3, Thomas E Yankeelov4
1School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
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 (TGV 2), 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 v (extravascular-extracellular volume fraction) across a population of random sampling schemes.
Results:
NN produced the lowest image error (SER: 29.1), while TV/TGV 2 produced the most accurate Ktrans (CCC: 0.974/0.974) and v (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/TGV 2 should be used as temporal constraints for CS DCE-MRI of the breast.
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