Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study

Abstract

Purpose: To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging. Methods: Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant K trans in the extended Tofts model and blood flow F B and blood volume V B from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated. Results: The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average crosscorrelation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data. Conclusion: With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.

Accelerating volumetric cine MRI (VC-MRI) using undersampling for real-time 3D target localization/tracking in radiation therapy: a feasibility study

“…It is also worthy to mention that only retrospective patient data are included in the current study to evaluate the feasibility of accelerating the VC-MRI technique. Compared to prospective studies, retrospective studies have the advantages of providing the ground-truth images for evaluation and having the flexibility to investigate the effects of various under sampling strategies as well as various amounts of under sampling(Sarma et al , 2014; Subashi et al , 2013; Wang et al , 2016). However, retrospective studies do not account for certain challenges in prospective in-vivo applications of the technique, such as undersampling artifacts, timing errors, gradient imperfections and Eddy currents in the MR acquisition.…”

“…constant azimuthal increment of 111.25°, with spoke numbers N = 32 and 21 used to sample the 2D cine MRI k-space data. The spoke numbers were chosen based on previous publications (Wang et al , 2016). This golden angle of 111.25° is related to the Golden Ratio and causes radial lines to be evenly spaced with time (Winkelmann et al , 2007).…”

Temporal Huber Regularization for DCE-MRI

“…TGV was first used in MRI as a spatial prior in [23]. TGV has also been used in MRI as a temporal prior in [44], where different temporal priors were compared in cartesian DCE-MRI of the breast.…”

“…The staircasing effect could be alleviated by using L 2norm based temporal smoothness (TS) regularization [4] or total generalized variation (TGV) regularization which promotes piecewise linear solutions [7]. Both TS and TGV have been used in CS DCE-MRI [4,44,45]. In [35], TS was used in combination with a spatial regularization term which used infimal convolution of two total variation Bregman distances for incorporating structural a priori information from an anatomical prior image into the reconstruction of the dynamic image sequence.…”

4D-MRI in Radiotherapy

“…A possible improvement is view sharing, which enable the use of same data in multiple volumes. Such technique has been widely used in dynamic MRI imaging for pharmacokinetics study [51,52]. Because of motion sensitivity of low-frequency k-space component, view sharing of high-frequency component in combination with iterative reconstruction is a viable solution [35].…”