I-Corps: Software-based approach enabling faster magnetic resonance imaging scans

该I-Corps项目的更广泛影响/商业潜力是减少磁共振成像（MRI）扫描时间并提高患者吞吐量，这将有利于患者和医疗保健提供者。 MRI被广泛用作临床诊断工具。 美国每年进行超过3000万次MRI扫描。 然而，需要MRI扫描的患者通常面临较长的等待时间，根本原因是每个患者的扫描时间，这是限制每日吞吐量的根本瓶颈。 该项目开发了一种基于软件的方法来减少MRI扫描时间。 更短的扫描时间将提高患者的舒适度。 提高吞吐量将增加可访问性，使患者能够及时接受MRI扫描，减少等待时间，并有助于改善医疗保健结果。 对于医疗保健提供商和成像设施来说，更短的扫描时间和更高的吞吐量将提高运营效率、收入潜力以及患者满意度。 除了在MRI中的应用外，该技术还可以适用于其他需要强大信号去噪的应用，例如视频流和石油天然气勘探。I-Corps项目探索了基于软件的方法的商业潜力，以减少MRI扫描时间并增加患者吞吐量。 该技术可以更有效地处理MRI数据，并且可以与现有仪器集成，而无需任何硬件修改。 在MRI中，图像质量取决于信噪比，信噪比由信号平均值/采集次数（测量每个数据点的次数）决定。 来自组织和扫描仪的噪声的存在需要重复测量，以便消除噪声以产生高质量的图像。 如果可以通过信号处理而不是重复测量来去除噪声，则可以在保持图像质量的同时减少扫描时间。 与传统的信号处理方法，如时间平均，滤波和其他去噪方法相比，这里开发的方法可以处理含噪信号，而不会受到去除太多或太少噪声的限制，从而使维持图像质量所需的测量次数最小化，这最终减少了整个扫描。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。

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.