Hassan Haji-valizadeh^{1,2}, Joshua D. Robinson^{3,4}, Michael Markl^{2}, Cynthia K. Rigsby^{2,5}, and Daniel Kim^{2}

Iterative compressed sensing reconstruction of real-time phase-contrast MR images acquired with highly-accelerated radial k-space sampling produces considerable image blurring. We propose a Cartesian Golden-angle radial sparse parallel (GRASP) framework that achieves a good balance between image reconstruction speed and data fidelity. The performance of the proposed reconstruction framework is compared with the original GRASP and GROG-GRASP frameworks using 38.4-fold accelerated phase-contrast MRI data acquired from pediatric patients.

Introduction

Real-time, phase-contrast (rt-PC) MRI has the potential to overcome the limitations, such as sensitivity to arrhythmia and patient motion, associated with standard ECG-gated PC MRI.Method

Pulse Sequence: We
evaluated our proposed rt-PC acquisition and reconstruction methods in 7
pediatric patients (3 boys, mean age = 9.4 ± 2.6 years) undergoing a clinical
cardiac MRI at 1.5T scanner (Siemens, Aera), which included standard PC MRI at up
to 4 locations (aortic valve, pulmonic valve, left pulmonary artery, right
pulmonary artery). Pertinent imaging parameters for clinical and rt-PC MRI are summarized
in Table 1. Additional imaging parameters for rt-PC include radial k-space
sampling with golden angle ratio = 111.246°.^{10}

Image
Reconstruction: As shown in Figure 1, we implemented a Cartesian GRASP framework
by first shifting the polar data onto a Cartesian image space using NUFFT^{7}, followed by estimating the
Cartesian k-space pattern using gridding. Coil-combined zero-filled images,
auto-calibrated sensitivity profiles^{11}, and Cartesian k-space
data were fed into the iterative CS framework during which artifacts were
removed using both temporal finite difference and temporal principal component(PCA)^{12} as two orthogonal sparsifying
transforms, where the regularization weight for PCA was 10 times smaller than
that for temporal finite difference. The optimal normalized weights Cartesian
GRASP, GRASP, and GROG-GRASP were determined empirically using visual analysis
of training data set, and CS converged to a solution using 33 iterations of non-linear conjugate gradient with back-tracking line search. A single iteration using block matching (BM) filter^{9 }was applied separately for
both the magnitude and phase images to remove residual artifacts.

Data Analysis: To calculate forward flow, reverse flow, and peak velocity for a single representative heartbeat, region of interests were drawn manually on a workstation equipped with commercial software (CVi42, Circle Cardiovascular Imaging). Statistical analyses included ANOVA (Bonferroni with clinical as control), linear regression, and Bland-Altman.

Results** **

Conclusion

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