Giovannetti Giulio, Fontana Nunzia, Flori Alessandra, Santarelli Maria Filomena, Tucci Mauro, Positano Vincenzo, Barmada Sami, Frijia Francesca
Institute of Clinical Physiology, National Research Council (CNR), 56124 Pisa, Italy.
Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56126 Pisa, Italy.
Sensors (Basel). 2024 Mar 19;24(6):1954. doi: 10.3390/s24061954.
Radiofrequency (RF) coils for magnetic resonance imaging (MRI) applications serve to generate RF fields to excite the nuclei in the sample (transmit coil) and to pick up the RF signals emitted by the nuclei (receive coil). For the purpose of optimizing the image quality, the performance of RF coils has to be maximized. In particular, the transmit coil has to provide a homogeneous RF magnetic field, while the receive coil has to provide the highest signal-to-noise ratio (SNR). Thus, particular attention must be paid to the coil simulation and design phases, which can be performed with different computer simulation techniques. Being largely used in many sectors of engineering and sciences, machine learning (ML) is a promising method among the different emerging strategies for coil simulation and design. Starting from the applications of ML algorithms in MRI and a short description of the RF coil's performance parameters, this narrative review describes the applications of such techniques for the simulation and design of RF coils for MRI, by including deep learning (DL) and ML-based algorithms for solving electromagnetic problems.
用于磁共振成像（MRI）的射频（RF）线圈用于产生RF场，以激发样品中的原子核（发射线圈），并接收原子核发射的RF信号（接收线圈）。为了优化图像质量，必须使RF线圈的性能最大化。特别是，发射线圈必须提供均匀的RF磁场，而接收线圈必须提供最高的信噪比（SNR）。因此，必须特别关注线圈仿真和设计阶段，这可以通过不同的计算机仿真技术来执行。机器学习（ML）在工程和科学的许多领域中广泛应用，是线圈仿真和设计的不同新兴策略中一种很有前途的方法。从ML算法在MRI中的应用以及对RF线圈性能参数的简短描述开始，这篇叙述性综述描述了此类技术在MRI的RF线圈仿真和设计中的应用，包括用于解决电磁问题的深度学习（DL）和基于ML的算法。
