Zou Jiaren, Jiang Yun, Kaplan Sydney, Seiberlich Nicole, Cao Yue
IEEE Trans Med Imaging. 2025 Aug;44(8):3185-3195. doi: 10.1109/TMI.2025.3559467.
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.
这项工作旨在通过开发一种计算高效的基于模型的深度学习（MBDL）图像重建框架以及图像重建、定量参数估计和k空间采样轨迹的联合优化框架，来提高扫描效率，并克服全3D螺旋轨迹的高分辨率磁共振指纹识别（MRF）中的计算挑战。使用参数估计损失来联合优化图像重建和参数量化网络。此外，通过学习MRF数据的解剖学特定时空稀疏性，对全3D螺旋轨迹的旋转角度进行数据驱动的优化，并与图像重建网络训练联合进行。使用在健康受试者和患者中采集的模拟和体内MRF数据对MBDL图像重建进行评估，并与局部低秩（LLR）迭代重建进行比较。与来自2分钟和1分钟扫描的模拟和体内MRF数据的LLR重建相比，由MBDL重建的全脑、1毫米各向同性、T1和T2图像体积改善了参数的归一化均方根误差（NRMSE）（高达30%），并减少了重建时间（高达65倍）。图像-参数重建或采样轨迹-图像重建的联合优化在模拟数据上从基线MBDL重建显著进一步改善了T1和T2的NRMSE（p<0.05）。这项工作开发了一个通用的端到端框架，通过以最少的计算和时间需求联合优化图像重建、参数重建和采样轨迹，来提高3D定量MRI的参数量化精度并缩短重建时间。
