AI-Driven Design of RF Pulses for Enhancing Nuclear Magnetic Resonance Spectroscopy and Imaging

在化学系化学测量和成像（CMI）计划的支持下，明尼苏达大学的Gianluigi Veglia教授及其团队正在开发新的射频（RF）辐照技术，以提高化学结构的重要探针的性能，包括核磁共振（NMR）光谱和磁共振成像（MRI）。具体来说，Veglia团队正在利用强大的软件调用人工智能来协助设计过程。由此产生的方法有可能使生物聚合物的化学和结构分析的更高质量的数据的采集，因此，预计将提高MRI的图像质量和采集时间。所制定的方法将免费提供给科学界。从事研究的本科生和研究生将接受严格的培训，了解磁共振的基本和高级概念，并介绍如何使用人工智能进行优化过程。Veglia集团最近开发了新的软件（GENETICS-AI），可生成高度补偿的RF脉冲形状，具有前所未有的保真度，适用于NMR和MRI。新的RF形状固有地是宽带的，具有可调谐的自旋操作保真度。新的RF脉冲已经基准使用经典的自旋纠缠问题，获得99.999%的保真度水平。液体和固体核磁共振实验的应用导致更高的信号噪声水平的多维光谱。该团队目前正在开发具有相位和幅度调制的RF脉冲的能力，使用进化算法和人工智能例程的后续训练来优化新RF形状的功率和相位。最终目标是获得高保真脉冲和脉冲序列，这些脉冲和脉冲序列将在高磁场和超高磁场下运行的商用NMR光谱仪或MRI扫描仪上实现。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。

With support from the Chemical Measurement and Imaging (CMI) Program in the Division of Chemistry, Professor Gianluigi Veglia and his group at the University of Minnesota are developing new radiofrequency (RF) irradiation techniques to enhance the performance of important probes of chemical structure, including nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance imaging (MRI). Specifically, the Veglia group is utilizing powerful software that invokes artificial intelligence to assist the design process. The resulting methods have the potential to enable the acquisition of higher quality data for chemical and structural analysis of biopolymers and, as a result, are expected to improve image quality and acquisition times in MRI. The methods developed will be made freely available to the scientific community. Undergraduate and graduate students engaged in the research will receive rigorous training in basic and advanced concepts in magnetic resonance and an introduction to the use of artificial intelligence for optimization processes. The Veglia group has recently developed new software (GENETICS-AI) that generates highly compensated RF pulse shapes with unprecedented levels of fidelity for applications to NMR and MRI. The new RF shapes are inherently broadband, with tunable fidelity of spin operation. The new RF pulses have been benchmarked using a classical spin entanglement problem, obtaining a fidelity level of 99.999%. The application to liquid and solid-state NMR experiments resulted in higher signal-to-noise levels for multidimensional spectra. The team is now developing capabilities for RF pulses with both phase and amplitude modulations using an evolutionary algorithm and subsequent training of an artificial intelligence routine to optimize the power and phase of new RF shapes. The ultimate goal is to obtain high-fidelity pulses and pulse sequences that will be implemented on commercial NMR spectrometers or MRI scanners operating at high and ultra-high magnetic fields.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.