Dawood Peter, Blaimer Martin, Herrler Jürgen, Liebig Patrick, Weinmüller Simon, Malik Shaihan, Jakob Peter M, Zaiss Moritz
Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Experimental Physics 5, University of Würzburg, Würzburg, Germany.
Magn Reson Med. 2025 Sep;94(3):1026-1043. doi: 10.1002/mrm.30533. Epub 2025 May 23.
To non-heuristically identify dedicated variable flip angle (VFA) schemes optimized for the point-spread function (PSF) and SNR of multiple tissues in 3D FSE sequences with very long echo trains at 7T.
The proposed optimization considers predefined specific absorption rate (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.
PSF-optimized VFAs resulted in significantly reduced image blurring compared to standard VFAs for T2-weighted while maintaining contrast fidelity. Small white and gray matter structures, as well as blood vessels, were more visible with PSF-optimized VFAs. Quantitative analysis shows that the optimized VFA yields 50% less deviation from a reference PSF (sinc) than the standard VFA. The SNR-optimized VFAs yielded images with significantly improved SNR in a white and gray matter region relative to standard (77.1 vs. 40.7, respectively) as trade-off for elevated image blurring.
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.
在7T场强下，针对具有极长回波链的3D快速自旋回波（FSE）序列，非启发式地识别针对多个组织的点扩散函数（PSF）和信噪比（SNR）进行优化的专用可变翻转角（VFA）方案。
所提出的优化方法使用端到端学习框架，考虑预定义的比吸收率（SAR）约束和目标对比度。成本函数整合了对比度保真度（SNR）的组件以及一个惩罚项，以最小化多个组织的图像模糊（PSF）。通过调整PSF/SNR成本函数组件的权重，推导了PSF和SNR优化的VFA，并在两名志愿者身上使用开源的Pulseq标准以及在三名志愿者身上使用具有并行发射扩展的7T磁共振成像（MRI）系统上的供应商协议进行了体内测试。
与T2加权的标准VFA相比，PSF优化的VFA在保持对比度保真度的同时，显著减少了图像模糊。PSF优化的VFA使小的白质和灰质结构以及血管更清晰可见。定量分析表明，优化后的VFA与参考PSF（辛格函数）的偏差比标准VFA小50%。作为图像模糊增加的权衡，SNR优化的VFA在白质和灰质区域产生的图像SNR显著提高（分别为77.1和40.7）。
本研究证明了端到端学习框架在7T场强下针对3D FSE采集的极长回波链中优化VFA方案在PSF和SNR方面的潜力。它为3D FSE在PSF和SNR之间的权衡进行快速灵活调整铺平了道路。
