Objective.Imaging dynamic objects with high temporal resolution is challenging in magnetic resonance imaging (MRI). The partial separable (PS) model was proposed to improve imaging quality by reducing the degrees of freedom of the inverse problem. However, the PS model still suffers from a long acquisition time and an even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously.Approach.We proposed to fully exploit the dimension-reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on a public dataset and prospective experiments onin vivodata. The proposed method was compared with four competing algorithms based on the PS model and two non-PS model methods.Main results.The proposed method has robust performance against a shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+sparse method, enabling image reconstruction to be finished in just a few seconds.Significance.The accelerated PS model has the potential to save a great deal of time in clinical dynamic MRI examinations and is promising for real-time MRI applications.
Keywords: dimension reduction; dynamic magnetic resonance imaging; image reconstruction; low-rank model; partial separable model.
© 2023 Institute of Physics and Engineering in Medicine.