Advanced Deep Learning Approaches to Enhance Magnetic Resonance Tomography

断层扫描起源于20世纪的射电天文学，是无损检测、地震研究、医学物理学和生理学等现代应用的基础。这些多学科应用不仅源于相同的核心图像科学原理，而且代表了现代重要物理发现的新应用。磁共振成像是将核磁共振的发展与图像科学的层析成像概念相结合的一个重要例子。我以前的NSERC研究主要集中在改进MR断层扫描，特别是提高图像质量和吞吐量。磁共振成像是一种理想的非侵入性生物系统成像方法。一个基本的挑战仍然是数据采集时间长。为了克服这一限制，我探索了适当的数据欠采样方法。将这些与先进的图像重建方法相结合，可以快速获得高质量的图像。我使用压缩感知等先进方法取得了很大的成功。自2014年以来，培养了32名高素质人才，其中3人开始担任教职。2018年，我的团队很快认识到深度学习（DL）在断层扫描中的作用。利用我丰富的图像科学专业知识，我开创了先进的深度学习解决方案来重建采样不足的MR图像数据（6篇出版物）。在下一个拨款周期中，我的总体目标是探索使用DL方法获取和重建MR数据之间的联系。我的目标是继续提高MR图像质量和吞吐量。我将重点关注3个关键目标：使用复杂值网络并建立适当策划的原始MR数据库。2.研究数据采样策略对dl重建图像质量的影响。3.探讨客观评价和主观评价图像质量之间的关系。Obj 1的重点是确保我们的研究使用支持复数的尖端深度学习框架，并确保访问大量数据。Obj 2将批判性地重新评估当前DL研究中使用的许多假设和简化，例如伪随机采样模式的使用和多线圈成像阵列设计的约束。我希望重新考虑这些因素将改善图像质量。最后，Obj 3将使用客观指标和主观（人）评估进行关联评估，以更好地理解这些指标之间的关系，并找到更好地匹配主观表现的客观指标。在接下来的5年里，我将改进用于MR成像的深度学习网络，并期望这些知识将对提高图像质量和吞吐量产生直接影响。从长远来看，通过现有的和新的合作，我将扩大应用到其他领域，并将继续在对加拿大学术和商业部门重要的新兴领域培训HQP。

With origins in 20th century radio-astronomy, tomography is the basis of modern applications ranging from non-destructive testing, to seismic studies, to medical physics and physiology. These multi-disciplinary applications are not only derived from the same core image science principles, but also represent novel applications of important modern physics discoveries. Magnetic resonance (MR) imaging is an important example that combines developments in nuclear magnetic resonance with tomographic concepts from image science. My previous NSERC research has focussed on improving MR tomography, specifically, on enhancing image quality and throughput. MR imaging is an ideal non-invasive method for imaging biological systems. A fundamental challenge remains the long data acquisition time. To overcome this limitation, I have explored appropriate data undersampling methods. Combining these with advanced image reconstruction methods may allow high-quality images to be obtained rapidly. I have met with much success using advanced approaches like compressed sensing. Since 2014, 32 highly qualified personnel (HQP) have been trained, including three who started faculty positions. In 2018, my group was quick to recognize the role of deep learning (DL) in tomography. Leveraging my substantial image science expertise, I have pioneered advanced DL solutions to reconstruct undersampled MR image data (6 publications). In this next grant cycle, my overall goal is to explore links between acquisition and reconstruction of MR data when using DL methods. My goal is to continue to improve MR image quality and throughput. I will focus on 3 key objectives: 1.Use complex-valued networks and establish appropriately curated raw MR databases. 2.Investigate the role of data sampling strategies on the quality of DL-reconstructed images. 3.Explore the relationship between objective and subjective assessments of image quality. Obj 1 focuses on ensuring that our studies use cutting-edge DL frameworks that support complex numbers and ensuring access to large volumes of data. Obj 2 will critically re-appraise many of the assumptions and simplifications used in current DL studies, such as the use of pseudo-random sampling patterns and constraints on the design of multi-coil imaging arrays. I expect reconsideration of these factors will improve image quality. Finally, Obj 3 will correlate assessments made using objective metrics and subjective (human) assessments, to better understand the relationship between such measures and to find objective measures that better match subjective performance. Over the next 5 years, I will have improved DL networks for MR imaging and expect that this knowledge will have a direct impact on enhancing image quality and throughput. In the longer term and through existing and new collaborations, I will broaden applications to other areas and will continue to train HQP in emerging areas of importance to Canada's academic and commercial sectors.