期刊
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
卷 9, 期 -, 页码 917-927出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCI.2023.3324757
关键词
Dynamical systems; magnetic resonance imaging; real-time imaging; spectro-dynamic MRI; time-resolved imaging
This study presents an extended Spectro-Dynamic MRI framework that can reconstruct time-resolved MR images and dynamical parameters at a high temporal resolution. The proposed method, solved by an iterative algorithm, outperforms the traditional two-step approach. Experimental results show that this method can accurately reconstruct all dynamic variables under different sampling patterns.
Measuring the dynamics and mechanical properties of muscles and joints is important to understand the (patho)physiology of muscles. However, acquiring dynamic time-resolved MRI data is challenging. We have previously developed Spectro-Dynamic MRI which allows the characterization of dynamical systems at a high spatial and temporal resolution directly from k-space data. This work presents an extended Spectro-Dynamic MRI framework that reconstructs 1) time-resolved MR images, 2) time-resolved motion fields, 3) dynamical parameters, and 4) an activation force, at a temporal resolution of 11 ms. An iterative algorithm solves a minimization problem containing four terms: a motion model relating the motion to the fully-sampled k-space data, a dynamical model describing the expected type of dynamics, a data consistency term describing the undersampling pattern, and finally a regularization term for the activation force. We acquired MRI data using a dynamic motion phantom programmed to move like an actively driven linear elastic system, from which all dynamic variables could be accurately reconstructed, regardless of the sampling pattern. The proposed method performed better than a two-step approach, where time-resolved images were first reconstructed from the undersampled data without any information about the motion, followed by a motion estimation step.
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