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

开发基于 20 秒长深度学习的全自动校准程序，用于超高场 MR 人体成像中的并行传输 (pTx)

• 批准号: 524729317
• 负责人: Dr. Sebastian Schmitter
• 金额: --
• 依托单位: Fachbereich 8.1 - Biomedizinische Magnetresonanz
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/524729317?language=en
• 关键词: Development; fully; automatic; 20; second

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.

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特斯拉及以上运行的所谓超高场(UHF)核磁共振扫描仪正越来越多地被研究用于临床和科学目的，其中包括更高的空间图像分辨率和更快的图像采集。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特斯拉下对身体进行成像的好处还很少被研究，它还没有被批准用于常规临床检查。造成这一现象的主要原因和超高频人体磁共振成像的一个主要问题是图像的空间高度不均匀信号。这种效应是由于射频天线为成像核自旋而照射的射频场的磁分量(B1+)的不均匀分布引起的。虽然这种不均匀的分布可以通过所谓的“并行传输(PTX)”来非常成功地补偿，即通过使用多个天线并由单独的RF脉冲独立地驱动它们，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+MAP，以及ii)必须计算PTX RF脉冲。这种方法的缺点是在体内的校准时间通常很长，通常为10-15分钟，其中大部分时间落在步骤I)。正是这一校准时间严重阻碍或阻止了科学研究和临床UHF在体内的应用。大量减少这一时间是本应用程序的目标。拨款申请是基于申请者小组最近提出的一项初步技术，其中B1+图不需要通过单独的扫描进行测量。相反，通过使用神经网络(NN)，地图是从定位地图估计的，无论如何，这是在研究开始时获得的。在这一应用中，这项技术将得到进一步的发展，并将从精度和稳健性方面进行系统的分析。此外，在与奥胡斯大学的拼贴画联合进行的另一项工作中，PTX RF脉冲的计算也使用神经网络进行了大规模加速。在目前的赠款申请中，后一种技术将与先进的基于神经网络的B1+映射方法相结合，形成单个基于人工智能的校准步骤，不需要超过20秒的校准时间。这一新方法将在柏林、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超高频患者研究，而无需冗长的校准，这将促进未来的患者研究，以调查7特斯拉对人体的好处。