Abstract
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.
Highlights
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) involves the continuous acquisition of T1weighted MR images during and after the injection of a paramagnetic contrast agent (CA)
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 (TGV2α), and nuclear norm (NN)
We found that Nuclear norm (NN) produced the highest signal-to-error ratio (SER) (29.1, higher is better) among the five constraints tested while WT produced the lowest (21.8)
Summary
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) involves the continuous acquisition of T1weighted MR images during and after the injection of a paramagnetic contrast agent (CA). Across serial images each image voxel yields an intensity time course that can be used to estimate physiological parameters, such as the volume transfer constant, Ktrans, and extravascular-extracellular volume fraction, Ve [1, 2]. Both high temporal and spatial resolutions are beneficial in DCE-MRI; high temporal resolution is needed for quantitative DCE-MRI analysis, while high spatial resolution aids clinical reading. The requirement for repeated, high signal-to-noise images limits simultaneous enhancement of temporal and spatial resolutions by conventional data acquisition methods.
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