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

用于增强核磁共振波谱和成像的人工智能驱动射频脉冲设计

• 批准号: 2304829
• 负责人: Gianluigi Veglia
• 金额: $ 54万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304829&HistoricalAwards=false
• 关键词: AI; Driven; Design; RF; Pulses

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.

在化学系化学测量和成像（CMI）项目的支持下，明尼苏达大学的Gianluigi Veglia教授和他的团队正在开发新的射频（RF）照射技术，以提高重要化学结构探针的性能，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来调用人工智能来辅助设计过程。由此产生的方法有可能为生物聚合物的化学和结构分析获得更高质量的数据，因此，有望提高MRI的图像质量和采集时间。开发的方法将免费提供给科学界。参与研究的本科生和研究生将接受严格的磁共振基础和高级概念培训，并介绍人工智能在优化过程中的应用。Veglia小组最近开发了一种新的软件（GENETICS-AI），该软件可以生成高度补偿的RF脉冲形状，具有前所未有的保真度，适用于核磁共振和核磁共振。新的射频形状本身是宽带的，具有可调的自旋操作保真度。利用经典的自旋纠缠问题对新的射频脉冲进行了基准测试，获得了99.999%的保真度。该方法在液相和固态核磁共振实验中的应用提高了多维谱的信噪比。该团队目前正在开发具有相位和幅度调制的射频脉冲功能，使用进化算法和随后的人工智能常规训练来优化新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商业核磁共振光谱仪或核磁共振扫描仪上实现，这些扫描仪在高磁场和超高磁场下工作。该奖项反映了美国国家科学基金会的法定使命，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。