The broader impact/commercial potential of this I-Corps project is to reduce Magnetic Resonance Imaging (MRI) scan-times and improve patient throughput, which will be beneficial for patients as well as healthcare providers. MRI is widely used as a clinical diagnostic tool. Over 30 million MRI scans are conducted each year in the US. However, patients in need of MRI scans often face long wait-times, a root cause being the per-patient scan-time which is a fundamental bottleneck that limits daily throughput. This project develops a software-based approach to reduce MRI scan-times. Shorter scan-times will improve patient comfort. Improved throughput will increase accessibility, allow patients to receive MRI scans in a timely manner with less wait-time, and contribute to better healthcare outcomes. For healthcare providers and imaging facilities, shorter scans and better throughput will increase operational efficiency, revenue potential, as well as patient satisfaction. In addition to application in MRI, this technology can potentially be adapted for deployment in other applications that require robust signal denoising, such as video streaming and oil and gas exploration.
This I-Corps project explores the commercial potential of a software-based approach to reduce MRI scan-time and increase patient throughput. The technology is leveraged to process MRI data in a more efficient way and can be integrated with existing instrumentation without requiring any hardware modifications. In MRI, image quality is dependent on the signal-to-noise ratio, which is governed by the number of signal averages/acquisitions (number of times each datapoint is measured). The presence of noise from tissue and the scanner requires repeated measurements in order to cancel out the noise to yield a high-quality image. If noise can be removed through signal processing instead of repeated measurements, the scan-time can be reduced while maintaining image quality. Compared to conventional signal processing methods such as time averaging, filtering, and other denoising methods, the method developed here can process noisy signals without the limitations of removing either too much or too little noise, thereby minimizing the number of measurements needed to maintain image quality which ultimately reduces the overall scan-time.
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.
