On the use of low-dimensional temporal subspace constraints to reduce reconstruction time and improve image quality of accelerated 4D-MRI

Background and purpose: The purpose of this work is to investigate the use of low-dimensional temporal subspace constraints for 4D-MRI reconstruction from accelerated data in the context of MR-guided online adaptive radiation therapy (MRgOART). Materials and methods: Subspace basis functions are derived directly from the accelerated golden angle radial stack-of-stars 4D-MRI data. The reconstruction times, image quality, and motion estimates are investigated as a function of the number of subspace coefficients and compared with a conventional frame-by-frame reconstruction. These experiments were performed in five patients with four 4D-MRI scans per patient on a 1.5T MR-Linac. Results: If two or three subspace coefficients are used, the iterative reconstruction time is reduced by 32% and 18%, respectively, compared to conventional parallel imaging with compressed sensing reconstructions. No significant difference was found between motion estimates made with the subspaceconstrained reconstructions (p > 0.08). Qualitative improvements in image quality included reduction in apparent noise and reductions in streaking artifacts from the radial k-space coverage. Conclusion: Incorporating subspace constraints for accelerated 4D-MRI reconstruction reduces noise and residual undersampling artifacts in the images while reducing computation time, making it a strong candidate for use in clinical MRgOART workflows. (c) 2021 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 158 (2021) 215-223

Automated summary

The study aimed to investigate the use of low-dimensional temporal subspace constraints for accelerated 4D-MRI reconstruction, which can reduce noise and residual artifacts while decreasing computation time.