CAREER: Automated Full-Duplex RF Platform for Next-Generation Accessible MRI

This project aims to develop a new radiofrequency (RF) technology for magnetic resonance imaging (MRI) to significantly reduce its size and power requirement. The novel technology will make new MRI scanners more portable and affordable than current bulky and expensive MRI systems, expanding access to high-quality medical imaging for broader populations. The project addresses critical gaps in healthcare delivery, especially in resource-limited settings. The societal benefits of the new MRI scanners are substantial: improved diagnostic capabilities, reduced healthcare costs, and enhanced patient outcomes. The impacts extend beyond the healthcare to communities and future workforce training. The project integrates educational initiatives to inspire and train students in both RF engineering and biomedical engineering, fostering a new generation of interdisciplinary engineering workforce. The project's outreach activities to K-12 students include hands-on demonstrations using DIY MRI scanner model to spark curiosity and interest in learning more about MRI technologies. The dissemination of research findings and open-source design tools through publications and industry collaborations will accelerate the adoption of the new low-cost MRI technologies, boosting the project's impacts nationwide. The project seeks to pioneer a robust, adaptive simultaneous-transmit-and-receive (STAR) RF front-end technology that can address fundamental limits of RF transmit-receive isolation performance in MRI systems due to near-field electromagnetic (EM) coupling, impedance shifts, and patient motion. The research goals include developing a rigorous system-level modeling and optimization framework to predict STAR performance under dynamic human-body loading conditions, constructing a multi-channel, load-insensitive, fully automated STAR RF platform with adaptive cancellation and automatic impedance matching, and demonstrating proof-of-concept integration of low-cost portable MRI systems. The approach combines rigorous theoretical modeling with innovative hardware strategies, such as wearable, flexible RF coils and automatic impedance matching networks that adapt to body-induced changes. Multi-stage adaptive cancellation methods will ensure robust transmit-receive isolation across multiple channels, even in the presence of patient motion and electromagnetic coupling. The adaptive STAR technology operates with lower power requirement and eliminates RF energy waste in conventional MRI systems relying on high-power RF pulses, thereby reducing RF energy absorbed by the patient's body. The project will clarify the interplay between coil geometry, adaptive matching, and cancellation algorithms, establishing new design principles for stable STAR performance in clinical conditions. These advances will also guide the development of other biomedical applications using RF wireless technologies, such as wearable biosensors and implantable devices, advancing knowledge at the intersection of RF engineering and healthcare. 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. NSF Award ID: 2543101 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: SUNGMIN SOHN | Institution: Arizona State University, SCOTTSDALE, AZ | Award Amount: $575,942 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543101 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543101.html