SC-GROG followed by L+S reconstruction with multiple sparsity constraints for accelerated Golden-angle-radial DCE-MRI.

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science

Magnetic Resonance Imaging*/methods ; Algorithms* ; Contrast Media*/chemistry ; Image Processing, Computer-Assisted*/methods; Humans

Competing Interests: The authors have declared that no competing interests exist.
The GRASP (Golden-angle-radial Sparse Parallel MRI) is a contemporary method for reconstructing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This method combines the temporal incoherence of stack-of-stars Golden-angle-radial sampling pattern and acceleration capability of parallel MRI (PI) and compressed sensing (CS) for highly accelerated free-breathing DCE-MRI reconstruction. GRASP uses Temporal Total Variation (TV) norm as a sparsity transform to promote sparsity among multi-coil MRI data and Nonlinear Conjugate Gradient (NL-CG) algorithm to obtain an optimal solution. Additionally, GRASP uses NUFFT gridding to map Golden-angle-radial data to Cartesian grid before NL-CG based CS reconstruction. However, major limitations of GRASP include the temporal averaging effect due to Temporal TV, leading to a degradation in the dynamic contrast of DCE-MRI, and a high computational burden/reconstruction time due to repeated NUFFT gridding/degridding in NL-CG reconstruction. This paper introduces a novel approach to address limitations in GRASP reconstruction technique for free-breathing DCE-MRI. The proposed method combines SC-GROG gridding with low-rank plus sparse (L+S) reconstruction using multiple sparsity constraints for accelerated Golden-angle-radial DCE-MRI with improved temporal resolution and dynamic contrast. Monotone FISTA with variable acceleration (MFISTA-VA) is used to optimize the L+S optimization problem. Further, SC-GROG gridding is used to map Golden-angle radial data to Cartesian grid before L+S reconstruction. The proposed method is tested on two different 3T free-breathing in-vivo DCE-MRI datasets. Reconstruction results of the proposed method are evaluated by using: (i) convergence error, (ii) peak and mean values of arterial signal intensity in the selected region of interest (ROI) of DCE MR Images, and (iii) reconstruction time. Results show that the proposed method provides significant improvements in the reconstruction time and dynamic contrast than the conventional Golden-angle-radial DCE-MRI reconstruction techniques (i.e., GRASP, XD-GRASP). Furthermore, convergence analysis shows that integration of MFISTA-VA in L+S reconstruction provides faster convergence compared to conventional L+S reconstruction.
(Copyright: © 2025 Najeeb et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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Date Created: 20250214 Date Completed: 20250214 Latest Revision: 20250216

20250216

PMC11828349

10.1371/journal.pone.0318102

39951435