Development of a fully automatic, 20-second long deep-learning based calibration procedure for parallel transmission (pTx) in ultrahigh field MR body imaging

在临床磁共振成像（MRI）中，通常使用场强为1.5和3特斯拉的MRI扫描仪。此外，在7特斯拉及以上工作的所谓的超高场（UHF）MRI扫描仪越来越多地被研究用于临床用途以及用于科学目的，其允许更高的空间图像分辨率和更快的图像采集等。虽然7特斯拉的优势已经被用于头部或四肢的临床诊断应用，但在7特斯拉下对身体成像的益处很少被研究，并且尚未被批准用于常规临床检查。这种情况的主要原因和UHF身体MRI中的主要问题是图像的空间高度不均匀信号。这种效应是由射频天线辐射的射频场的磁分量（B1+）的不均匀分布引起的，以成像核自旋。虽然这种不均匀分布可以通过所谓的“并行传输（pTx）”（即通过使用多个天线并通过单独的RF脉冲独立地驱动它们）非常成功地补偿，但这需要两个校准步骤：在拍摄快速概览图像（定位器）之后，对于每个患者，i）必须测量每个天线的B1+图，以及ii）必须计算pTx RF脉冲。这种方法的缺点是在体内通常10-15分钟的长校准时间，其中大部分时间福尔斯落在步骤i）。正是这种校准时间严重阻碍或阻止了科学研究，也阻碍了人体中的临床UHF应用。大规模地减少这个时间是这个应用程序的目标。该资助申请是基于申请人小组最近提出的初步技术，其中B1+图不需要通过单独的扫描来测量。相反，通过使用神经网络（NN），从定位器图估计图，定位器图无论如何在研究开始时获得。在本申请中，该技术将进一步发展，并将系统地分析精度和鲁棒性。此外，在与奥胡斯大学的学院联合进行的一项单独工作中，pTx RF脉冲计算也使用NN进行了大规模加速。在目前的拨款申请中，后一种技术将与先进的基于NN的B1+映射方法相结合，形成一个基于AI的校准步骤，该步骤不需要超过20秒的校准时间。这种新方法将在柏林、海德堡和美国明尼阿普利斯的不同超高频中心进行测试。最终的校准方法首次允许进行针对人体的UHF患者研究，而无需冗长的校准，这将促进未来的患者研究，以研究7特斯拉对人体的益处。

In clinical magnetic resonance imaging (MRI), MRI scanners with a field strength of 1.5 and 3 Tesla are typically used. In addition, so-called ultra-high field (UHF) MRI scanners operating at 7 Tesla and beyond are increasingly being investigated for clinical use as well as for scientific purposes, which allow, amongst others, higher spatial image resolution and faster image acquisition. While the advantage of 7 Tesla is already used diagnostically for clinical applications in the head or extremities, the benefits of imaging the body at 7 Tesla has been investigated rather rarely and it has not yet been approved for routine clinical examinations. The main reason for this and a major problem in UHF body MRI is the spatially highly inhomogeneous signal of the image. This effect arises from the inhomogeneous distribution of the magnetic component (B1+) of the radiofrequency (RF) fields irradiated by the RF antenna to image the nuclear spins. Although this inhomogeneous distribution can be compensated very successfully by so-called "parallel transmission (pTx)" i.e. by using multiple antennas and driving them independently by separate RF pulses, this requires two calibration steps: after taking a fast overview image (localizer), for each patient i) the B1+ map for each antenna has to be measured and ii) the pTx RF pulses have to be calculated. A drawback of this approach is the long calibration time of typically 10-15 minutes in the body, most of which falls on step i). It is this calibration time that severely hinders or prevents not only scientific studies but also clinical UHF applications in the body. Massively reducing this time is the goal of this application. The grant application is based on a recently by the group of the applicant presented preliminary technique, in which the B1+ maps do not need to be measured by a separate scan. Instead, by using neural networks (NN) the maps are estimated from the localizer maps, that is anyway acquired at the beginning of the study. In this application, this technique will be developed further and will be systematically analyzed with respect to precision and robustness. Furthermore, in a separate work performed jointly with collages from Aarhus University, the pTx RF pulse calculation has been massively accelerated using NN, too. In the present grant application, the latter technique will be combined with the advanced NN-based B1+ mapping method to form a single AI-based calibration step that does not require more than 20 seconds of calibration time. This novel method will be tested at different UHF centers in Berlin, in Heidelberg and in Minneaplis, USA. The final calibration method allows for the first time to perform UHF patient studies targeting the human body without the need of lengthy calibration, which will promote future patient studies to investigate the benefit of 7 Tesla for the human body.