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

Abstract EN

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.