HCC: Medium: Shape Optimization for the Design and Simulation of Electromagnetic Systems

该项目旨在创建一个先进的软件工具，自动化电磁（EM）系统的设计和优化，特别强调磁共振成像（MRI）的基本医疗成像技术。 EM系统应用广泛，包括手机天线、5G网络和医疗成像设备。目前，这些系统的设计和优化是劳动密集型的，需要高水平的用户专业知识和互动。我们的软件旨在简化和自动化这一过程。该软件将通过解决MRI中的一个未决问题进行测试和确认：射频（RF）线圈设计的优化，这是MRI机器的关键部分，会显著影响所生成图像的质量。目前针对RF线圈的手动优化过程效率低下，结果不理想，导致扫描时间更长，准确度更低。该项目不仅将促进技术进步和改善EM系统的设计过程，而且还通过让来自不同学科的学生参与支持跨学科培训。此外，该项目在医疗保健、认知神经科学和其他依赖MRI性能的领域具有潜在的社会效益。该项目的目标是开发和验证用于EM系统形状优化的软件管道，特别关注自动化参数优化的正向模拟和逆问题。该提案结合了几何处理技术和先进的EM模拟，以自动进行这种优化。该方法将涉及可微EM模拟，加速机器学习和自动几何变化探索的形状建模的新技术。该项目的重点将是MRI机器中的RF线圈设计，特别是那些在高频（3 T和7 T）下工作的机器，现有的线圈设计只能达到最佳信噪比的70-80%。该团队将制造和测试优化的7 T线圈设计，并将其性能与商用射频线圈进行比较。该项目还将引入一种创新的伴随公式，用于高效的形状梯度计算，从而为具有数百个设计参数的EM系统实现基于梯度的优化。该项目的成果将包括一个用于EM系统设计和优化的开源软件套件，预计将影响其他研究项目，并为教育和培训资源做出贡献。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to create an advanced software tool that automates the design and optimization of electromagnetic (EM) systems, with particular emphasis on magnetic resonance imaging (MRI) an essential medical imaging technology. EM systems are broadly applied and include cell phone antennas, 5G networks, and medical imaging devices. Currently, the design and optimization of these systems are labor-intensive and require a high level of user expertise and interaction. Our software aims to simplify and automate the process. The software will be tested and validated by addressing an open problem in MRI: the optimization of radiofrequency (RF) coil design, a keyl part of MRI machines that significantly impacts the quality of images produced. Current manual optimization processes for RF coils are inefficient and results are suboptimal leading to longer scanning times and lower accuracy. This project will not only make advancements in technology and improve the design process of EM systems, but it also supports interdisciplinary training by involving students from various disciplines. Further, the project holds potential societal benefits in healthcare, cognitive neuroscience, and other sectors that rely on MRI performance.The project's objective is to develop and validate a software pipeline for the shape optimization of EM systems, particularly focusing on automating the forward simulation and the inverse problem of parameter optimization. The proposal combines geometry processing techniques and advanced EM simulations to automate this optimization. The approach will involve novel techniques for differentiable EM simulation, machine learning for acceleration, and shape modeling for automatic geometric variations exploration. The focus of the project will be on RF coil design in MRI machines, particularly those operating at high frequencies (3T and 7T), where existing coil designs only achieve 70-80% of the optimum signal-to-noise ratio. The team will fabricate and test an optimized 7T coil design and compare its performance with commercial RF coils. The project will also introduce an innovative adjoint formulation for efficient shape gradient computation, enabling gradient-based optimization for EM systems with hundreds of design parameters. The results of this project will include an open-source software suite for EM system design and optimization, and it is expected to impact other research projects and contribute to educational and training resources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.