序列自适应场不均匀性估计（SAFE）：用于增强MRF定量分析的$B_1^+$和$B_0$不均匀性回顾性估计与校正
Sequence adaptive field-imperfection estimation (SAFE): retrospective estimation and correction of $B_1^+$ and $B_0$ inhomogeneities for enhanced MRF quantification
AI总结：
针对MRF受$B_1^+$、$B_0$场不均匀性影响且传统校准扫描无法回顾性应用的问题，提出无需校准的序列自适应深度学习框架SAFE，可在3T无训练数据的任意MRF序列上实现场效应估计校正，还可拓展至其他定量序列。
摘要：
$B_1^+$和$B_0$场不均匀性会显著降低磁共振指纹成像（MRF）定量参数估计的准确性与鲁棒性。额外的$B_1^+$和$B_0$校准扫描可缓解该问题，但会增加扫描时长，且无法回顾性应用于已采集的数据。本文提出一种无需校准的序列自适应深度学习框架，可估计并校正任意MRF序列的$B_1^+$和$B_0$效应。我们在3T场强下的任意MRF序列上验证了其性能，这些序列此前无对应训练数据。该方法可应用于所有已采集及未来的MRF扫描，同时凸显了将该框架直接应用于其他定量序列的灵活性。
英文摘要：
$B_1^+$ and $B_0$ field-inhomogeneities can significantly reduce accuracy and robustness of MRF's quantitative parameter estimates. Additional $B_1^+$ and $B_0$ calibration scans can mitigate this but add scan time and cannot be applied retrospectively to previously collected data. Here, we proposed a calibration-free sequence-adaptive deep-learning framework, to estimate and correct for $B_1^+$ and $B_0$ effects of any MRF sequence. We demonstrate its capability on arbitrary MRF sequences at 3T, where no training data were previously obtained. Such approach can be applied to any previously-acquired and future MRF-scans. The flexibility in directly applying this framework to other quantitative sequences is also highlighted.
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