Sommer K, Amthor T, Doneva M, Koken P, Meineke J, Börnert P
Philips Research Europe, Röntgenstr. 24-26, 22335 Hamburg, Germany.
Philips Research Europe, Röntgenstr. 24-26, 22335 Hamburg, Germany.
Magn Reson Imaging. 2017 Sep;41:7-14. doi: 10.1016/j.mri.2017.06.015. Epub 2017 Jul 3.
Sequence optimization and appropriate sequence selection is still an unmet need in magnetic resonance fingerprinting (MRF). The main challenge in MRF sequence design is the lack of an appropriate measure of the sequence's encoding capability. To find such a measure, three different candidates for judging the encoding capability have been investigated: local and global dot-product-based measures judging dictionary entry similarity as well as a Monte Carlo method that evaluates the noise propagation properties of an MRF sequence. Consistency of these measures for different sequence lengths as well as the capability to predict actual sequence performance in both phantom and in vivo measurements was analyzed. While the dot-product-based measures yielded inconsistent results for different sequence lengths, the Monte Carlo method was in a good agreement with phantom experiments. In particular, the Monte Carlo method could accurately predict the performance of different flip angle patterns in actual measurements. The proposed Monte Carlo method provides an appropriate measure of MRF sequence encoding capability and may be used for sequence optimization.
序列优化和合适的序列选择在磁共振指纹成像（MRF）中仍是未被满足的需求。MRF序列设计中的主要挑战是缺乏对序列编码能力的合适度量。为找到这样一种度量，已研究了三种用于判断编码能力的不同候选方法：基于局部和全局点积的度量，用于判断字典条目相似性，以及一种评估MRF序列噪声传播特性的蒙特卡罗方法。分析了这些度量对于不同序列长度的一致性，以及在体模和体内测量中预测实际序列性能的能力。虽然基于点积的度量对于不同序列长度产生了不一致的结果，但蒙特卡罗方法与体模实验结果吻合良好。特别是，蒙特卡罗方法能够在实际测量中准确预测不同翻转角模式的性能。所提出的蒙特卡罗方法提供了一种对MRF序列编码能力的合适度量，可用于序列优化。
