用端到端学习优化7T脑部MRI的翻转角，提升图像清晰度和信噪比。
Controlling sharpness, SNR and SAR for 3D FSE at 7T by end-to-end learning
- 通过端到端学习自动设计翻转角方案，兼顾图像清晰度与信噪比。
- 优化后图像模糊减少50%，白质灰质等细微结构更清晰可见。
- 适合追求高分辨率或高信噪比的7T MRI研究者使用。
目的：在7T超高场强下，针对长回波链3D FSE序列，非启发式地寻找针对多组织点扩散函数（PSF）和信噪比（SNR）优化的专用可变翻转角（VFA）方案。方法：基于端到端学习框架，考虑预设的射频能量（SAR）约束与目标对比度，成本函数整合了对比度保真度（SNR）项和抑制图像模糊（PSF）的惩罚项。通过调节PSF/SNR权重，分别获得聚焦于PSF和SNR的VFA方案，并在两名志愿者上使用开源Pulseq标准及三名志愿者在配备并行发射的7T MRI系统上进行体内验证。结果：与标准VFA相比，PSF优化方案显著降低图像模糊，保持对比度保真度，小血管、白质灰质结构更清晰；定量分析显示，其与理想sinc型参考PSF偏差减少50%。而SNR优化方案在白质灰质区域的信噪比显著提升（81.2±18.4 vs. 41.2±11.5），代价是图像模糊增加。结论：该研究证明端到端学习可用于优化7T下长回波链3D FSE的VFA方案，在PSF与SNR之间实现快速灵活的权衡。
原文摘要 · Abstract (English)
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.
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