Eberhardt Boris, Poser Benedikt A, Shah N Jon, Felder Jörg
Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jüich, Germany; RWTH Aachen University, Aachen, Germany.
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
Z Med Phys. 2022 Aug;32(3):334-345. doi: 10.1016/j.zemedi.2021.12.003. Epub 2022 Feb 7.
Spoke trajectory parallel transmit (pTX) excitation in ultra-high field MRI enables B inhomogeneities arising from the shortened RF wavelength in biological tissue to be mitigated. To this end, current RF excitation pulse design algorithms either employ the acquisition of field maps with subsequent non-linear optimization or a universal approach applying robust pre-computed pulses. We suggest and evaluate an intermediate method that uses a subset of acquired field maps combined with generative machine learning models to reduce the pulse calibration time while offering more tailored excitation than robust pulses (RP). The possibility of employing image-to-image translation and semantic image synthesis machine learning models based on generative adversarial networks (GANs) to deduce the missing field maps is examined. Additionally, an RF pulse design that employs a predictive machine learning model to find solutions for the non-linear (two-spokes) pulse design problem is investigated. As a proof of concept, we present simulation results obtained with the suggested machine learning approaches that were trained on a limited data-set, acquired in vivo. The achieved excitation homogeneity based on a subset of half of the B maps acquired in the calibration scans and half of the B maps synthesized with GANs is comparable with state of the art pulse design methods when using the full set of calibration data while halving the total calibration time. By employing RP dictionaries or machine-learning RF pulse predictions, the total calibration time can be reduced significantly as these methods take only seconds or milliseconds per slice, respectively.
超高场磁共振成像中的辐条轨迹并行发射（pTX）激发能够减轻生物组织中由于射频波长缩短而产生的磁场不均匀性。为此，当前的射频激发脉冲设计算法要么采用采集场图并随后进行非线性优化，要么采用应用稳健预计算脉冲的通用方法。我们提出并评估了一种中间方法，该方法使用采集的场图子集与生成式机器学习模型相结合，以减少脉冲校准时间，同时提供比稳健脉冲（RP）更具针对性的激发。研究了基于生成对抗网络（GAN）采用图像到图像转换和语义图像合成机器学习模型来推断缺失场图的可能性。此外，还研究了一种采用预测性机器学习模型来解决非线性（双辐条）脉冲设计问题的射频脉冲设计。作为概念验证，我们展示了使用所建议的机器学习方法获得的模拟结果，这些方法是在体内采集的有限数据集上进行训练的。在校准扫描中采集的一半B图子集和用GAN合成的一半B图的基础上实现的激发均匀性，在使用全套校准数据时与现有脉冲设计方法相当，同时总校准时间减半。通过采用RP字典或机器学习射频脉冲预测，总校准时间可以显著减少，因为这些方法分别每切片只需几秒钟或几毫秒。
