Purpose: To non-heuristically identify dedicated variable flip angle (VFA) schemes optimized for the point-spread function (PSF) and signal-to-noise ratio (SNR) of multiple tissues in 3D FSE sequences with very long echo trains at 7T. Methods: The proposed optimization considers predefined SAR constraints and target contrast using an end-to-end learning framework. The cost function integrates components for contrast fidelity (SNR) and a penalty term to minimize image blurring (PSF) for multiple tissues. By adjusting the weights of PSF/SNR cost-function components, PSF- and SNR-optimized VFAs were derived and tested in vivo using both the open-source Pulseq standard on two volunteers as well as vendor protocols on a 7T MRI system with parallel transmit extension on three volunteers. Results: PSF-optimized VFAs resulted in significantly reduced image blurring compared to standard VFAs for T2w while maintaining contrast fidelity. Small white and gray matter structures, as well as blood vessels, are more visible with PSF-optimized VFAs. Quantitative analysis shows that the optimized VFA yields 50% less deviation from a sinc-like reference PSF than the standard VFA. The SNR-optimized VFAs yielded images with significantly improved SNR in a white and gray matter region relative to standard (81.2\pm18.4 vs. 41.2\pm11.5, respectively) as trade-off for elevated image blurring. Conclusion: This study demonstrates the potential of end-to-end learning frameworks to optimize VFA schemes in very long echo trains for 3D FSE acquisition at 7T in terms of PSF and SNR. It paves the way for fast and flexible adjustment of the trade-off between PSF and SNR for 3D FSE.

翻译：目的：针对7T下采用超长回波链的3D快速自旋回波序列，以非启发式方法识别专为多组织点扩散函数与信噪比优化的定制化可变翻转角方案。方法：所提出的优化方案通过端到端学习框架，综合考虑预定义的比吸收率约束与目标对比度。损失函数整合了对比度保真度（信噪比）分量以及用于最小化多组织图像模糊（点扩散函数）的惩罚项。通过调整点扩散函数/信噪比损失函数分量的权重，推导出点扩散函数优化与信噪比优化的可变翻转角方案，并分别采用开源Pulseq标准对两名志愿者、以及基于并行发射扩展的7T磁共振系统厂商协议对三名志愿者进行体内验证。结果：在T2加权成像中，与标准可变翻转角方案相比，点扩散函数优化方案在保持对比度保真度的同时显著降低了图像模糊度。白质与灰质细微结构以及血管在点扩散函数优化方案下显示更清晰。定量分析表明，优化后可变翻转角方案相比标准方案的sinc型参考点扩散函数偏差降低50%。信噪比优化方案虽以图像模糊度增加为代价，但在白质与灰质区域相比标准方案获得显著提升的信噪比（分别为81.2±18.4 vs. 41.2±11.5）。结论：本研究证明了端到端学习框架在优化7T下3D快速自旋回波超长回波链可变翻转角方案方面，对于点扩散函数与信噪比调控的潜力，为快速灵活调整3D快速自旋回波中点扩散函数与信噪比的权衡关系开辟了新途径。