Increasing the scanning time efficiency of magnetic resonance imaging systems through data integration and deep learning

从不完整或有噪声的采集数据中重建高保真图像是一个普遍存在的工程挑战，具有许多应用。深度学习方法已经成为图像重建的最新技术。在这种背景下，医学图像重建对于患者诊断和健康管理至关重要。然而，这些成像检查费用昂贵，而且在加拿大等待成像检查的时间很长，而且还在增加。例如，对于磁共振成像(MRI)扫描，平均等待时间为9.3周。这些漫长的核磁共振成像等待时间估计每年花费加拿大经济7亿美元。超过50%的新医学成像检查是用于健康筛查和监测疾病演变和预后的后续检查。然而，在图像采集和重建过程中，后续检查不考虑从先前检查中获得的信息。这项研究将集中在核磁共振检查，这是加拿大等待时间最长的诊断成像方式。我们将开发一个整体解决方案，整合以往MRI检查的信息，以减少后续检查的整体扫描时间。虽然患者的历史(过去)成像信息很容易通过现有的医疗基础设施获得，但这些成像文件的海量大小阻碍了这些图像的实时(当患者仍在MRI扫描仪内)重建。此外，重要的是要确保重建的图像不会偏向先前的MRI检查。我们的目标是利用深度学习减少整体后续MRI扫描时间。根据我们的可行性研究，我们预计后续测试的速度将提高四倍。该项目的成功实施将显著减少核磁共振成像的等待时间，从而为加拿大的医疗保健系统节省数百万美元。我们的方法可以很容易地扩展到其他成像方式，从而提供能够实现更集成和更高效的成像系统的景观。

Reconstruction of high-fidelity images from incomplete or noisy acquired data is a ubiquitous engineering challenge with many applications. Deep learning methods have become the state-of-the-art for image reconstruction. In this context, medical image reconstruction is essential for patient diagnostics and health management. However, these imaging exams are expensive, and wait times for imaging examinations are long and increasing in Canada. For example, for magnetic resonance imaging (MRI) scans, there is an average wait time of 9.3 weeks. These long MRI wait times are estimated to cost $700M/year to Canada's economy.Over 50% of new medical imaging exams are follow-up exams used for health screening and monitoring disease evolution and prognosis. Nevertheless, follow-up exams do not consider the information available from previous examinations during image acquisition and reconstruction. This research will focus on MRI exams, the diagnostic imaging modality with the longest wait times in Canada. We will develop a holistic solution to integrate the information of previous MRI exams to decrease the overall scanning time of follow-up exams. Although the patient's historical (past) imaging information is readily available through the existing healthcare infrastructure, the massive size of these imaging files hinders real-time (while the patients are still inside the MRI scanner) reconstruction of these images. Moreover, it is essential to ensure that the reconstructed images are not biased toward the previous MRI exam. Our goal is to reduce overall follow-up MRI scan times utilizing deep learning. Based on our feasibility studies, we expect to make follow-up exams up to four times faster. The successful implementation of this project will lead to millions of dollars in savings for Canada's healthcare systems by significantly reducing MRI wait times. Our methods can be easily extended to other imaging modalities, thus providing the landscape enabling more integrated and efficient imaging systems.