Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction
Motivation: Deep Neural Networks (DNNs) show promise in reconstructing undersampled MRI data but face challenges with non-Cartesian 3D multi-coil acquisitions due to high memory demands and training complexity.
Goal(s): To address the research gap in validating DNNs performances for non-Cartesian data in 3D multi-coil settings and assess undersampling patterns' impact on reconstruction quality.
Approach: We extend the Non-Cartesian Primal-Dual Network (NC-PDNet) for multi-coil 3D reconstruction, evaluating different training configurations and benchmarking four non-Cartesian undersampling patterns.
Results: NC-PDNet with GoLF-SPARKLING trajectory achieved gains of +2.43/+0.01 in PSNR/SSIM for 32-channel data, reconstructing a 1mm isotropic volume in 4.95 seconds using 5.49 GB GPU memory.
Impact: Achieving fast, high-quality reconstructions with reasonable GPU memory usage demonstrates our approach's viability for clinical research. Benchmarking the GoLF-SPARKLING trajectory against established non-Cartesian baselines in 3D multi-coil settings validates its future application in more challenging experiments, notably at higher resolutions.
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