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

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.

该项目旨在创建一种高级软件工具，该工具可以自动化电磁（EM）系统的设计和优化，并特别强调磁共振成像（MRI）是必需的医学成像技术。 EM系统广泛应用，包括手机天线，5G网络和医学成像设备。当前，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流程。该软件将通过解决MRI中的开放问题来测试和验证：RadioFquency（RF）线圈设计的优化，MRI机器的钥匙部分会显着影响产生的图像质量。 RF线圈的当前手动优化过程效率低下，结果是次优的，导致扫描时间更长，精度较低。该项目不仅将在技术方面取得进步并改善EM系统的设计过程，而且还通过参与来自各个学科的学生来支持跨学科培训。此外，该项目在医疗保健，认知神经科学以及其他依赖MRI性能的领域中具有潜在的社会利益。该项目的目标是开发和验证软件管道以优化EM系统的形状，尤其是专注于自动化远期模拟和参数优化的倒数问题。该提案结合了几何处理技术和高级EM模拟，以自动化此优化。该方法将涉及用于可区分的EM模拟，加速的机器学习以及用于自动几何变化探索的形状建模的新技术。该项目的重点将放在MRI机器中的RF线圈设计上，尤其是在高频（3T和7T）上运行的机器，现有线圈设计仅达到最佳信噪比的70-80％。该团队将制造和测试优化的7T线圈设计，并将其性能与商业RF线圈进行比较。该项目还将引入一种创新的伴随公式，以实现有效的形状梯度计算，从而为具有数百个设计参数的EM系统提供了基于梯度的优化。该项目的结果将包括用于EM系统设计和优化的开源软件套件，并有望影响其他研究项目并有助于教育和培训资源。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。