Improved Spiral Projection MR Fingerprinting via Memory-Efficient Synergic Optimization of 3D Spiral Trajectory, Image Reconstruction and Parameter Estimation (SOTIP)

This work aims to improve scan efficiency and overcome computational challenges in high-resolution MR fingerprinting (MRF) with full 3D spiral trajectory by developing a computationally efficient model-based deep learning (MBDL) image reconstruction framework and a joint optimization framework of image reconstruction, quantitative parameter estimation and k-space sampling trajectory. A parameter estimation loss was used to jointly optimize image reconstruction and parameter quantification networks. Also, data-driven optimization of rotation angles of full 3D spiral trajectories through learning anatomy-specific spatiotemporal sparsity of the MRF data was performed jointly with image reconstruction network training. The MBDL image reconstruction was evaluated using simulated and in vivo MRF data acquired in healthy subjects and patients and compared with a locally low rank (LLR) iterative reconstruction. Whole-brain, 1-mm isotropic, T1 and T2 image volumes reconstructed by the MBDL improved normalized root mean squared errors (NRMSEs) (up to 30%) of the parameters and reduced reconstruction time (up to 65-fold) compared with the LLR reconstruction from both simulated and in vivo MRF data of 2-min and 1-min scans. Joint optimization of image-parameter reconstruction or sampling trajectory-image reconstruction further improved NRMSEs of T1 and T2 significantly from the baseline MBDL reconstruction (p<0.05) on simulated data. This work develops a generic, end-to-end framework to improve parameter quantification accuracy and shorten reconstruction time of 3D quantitative MRI by joint optimization of image reconstruction, parameter reconstruction and sampling trajectory with minimal computation and time demand.