Rapid high-fidelity T2* mapping using single-shot overlapping-echo acquisition and deep learning reconstruction
Qinqin Yang, Lingceng Ma, Zihan Zhou, Jianfeng Bao, Qizhi Yang, Haitao Huang, Shuhui Cai, Hongjian He, Zhong Chen, Jianhui Zhong, Congbo Cai
Abstract
Purpose
To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid T2* mapping.
Methods
In GRE-MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k-space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end-to-end parametric mapping from overlapping-echo signals. GRE-MOLED uses pure GRE acquisition with a single echo train to deliver T2* maps less than 90 ms per slice. The self-registered B 0 information modulated in image phase was utilized for distortion-corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task-based FMRI experiments.
Results
The quantitative results of GRE-MOLED T2* mapping demonstrated good agreement with those obtained from the multi-echo GRE method (Pearson’s correlation coefficient = 0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan–rescan test. Enabled by deep learning reconstruction, GRE-MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE-MOLED also achieved a higher temporal SNR and BOLD sensitivity in task-based FMRI.
Conclusion
GRE-MOLED is a new real-time technique for T2* quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.