Serrallés José E Cruz, Giannakopoulos Ilias I, Wang Siqi, Chen Damien, Zint Daniel, Panozzo Daniele, Zorin Denis, Lattanzi Riccardo
Bernard and Irene Schwartz Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, 660 1st Ave, New York, 10016, NY, USA.
Courant Institute of Mathematical Sciences, New York University, 60 5th Ave, New York, 10011, NY, USA.
bioRxiv. 2025 Aug 6:2025.08.04.668545. doi: 10.1101/2025.08.04.668545.
The radiative characteristics of the radiofrequency receive coils dictate the signal-to-noise ratio (SNR) of magnetic resonance images. Despite the crucial importance of RF coils, the practical coil design process has remained a largely empirical one. This work introduces a novel optimization framework for rational coil design, which relies on a fully automated pipeline that combines rapid electromagnetic simulations, shape optimization and coil meshing. The objective function iteratively maximizes SNR performance in a target region of interest with respect to the ultimate intrinsic SNR, which is the theoretically highest SNR independent from any particular coil design. The forward simulation employs a fast electromagnetic solver based on coupled surface and volume integral equations. The coils are represented as B-spline curves with an associated width, and automatically meshed for EM simulation. We implemented a new method to tune and decouple coils at each iteration without manual user intervention. The algorithm optimizes the size and position of a given number of coils with a combination of grid search and a line search. We demonstrated the framework by designing receive arrays of increasing complexity that yield optimal SNR for different target regions inside a numerical head model. SNR simulation time ranged from 15 s for a 3-coil configuration to 32 s for a 12-coil array, constrained to a helmet-like surface, including tuning and decoupling. The optimized 12-coil geometry yielded 9% higher average SNR performance in the brain at 3 T. This work represents the first automated coil optimization framework that uses full-wave electromagnetic simulations and ultimate performance benchmarks. This novel approach enables the systematic design of coils for magnetic resonance imaging with significantly improved SNR performance, potentially transforming coil development from empirical design to physics-driven optimization.
射频接收线圈的辐射特性决定了磁共振图像的信噪比（SNR）。尽管射频线圈至关重要，但实际的线圈设计过程在很大程度上仍然是经验性的。这项工作引入了一种用于合理线圈设计的新型优化框架，该框架依赖于一个全自动流程，该流程结合了快速电磁模拟、形状优化和线圈网格化。目标函数相对于最终固有信噪比在目标感兴趣区域中迭代地最大化SNR性能，最终固有信噪比是理论上最高的信噪比，与任何特定的线圈设计无关。正向模拟采用基于耦合表面和体积积分方程的快速电磁求解器。线圈表示为具有相关宽度的B样条曲线，并自动网格化以进行电磁模拟。我们实现了一种新方法，可在每次迭代时对线圈进行调谐和解耦，而无需用户手动干预。该算法通过网格搜索和线搜索相结合的方式优化给定数量线圈的尺寸和位置。我们通过设计复杂度不断增加的接收阵列来展示该框架，这些阵列可为数值头部模型内的不同目标区域产生最佳SNR。SNR模拟时间范围从3线圈配置的15秒到12线圈阵列的32秒，限制在类似头盔的表面，包括调谐和解耦。优化后的12线圈几何形状在3T时大脑中的平均SNR性能提高了9%。这项工作代表了第一个使用全波电磁模拟和最终性能基准的自动线圈优化框架。这种新颖的方法能够系统地设计用于磁共振成像的线圈，显著提高SNR性能，有可能将线圈开发从经验设计转变为物理驱动的优化。
