CAREER: Geometric Techniques for Big Data Medical Imaging

医学成像已经极大地受益于信号和图像处理的进步，这使得能够实现更好的数据采集、上级重建和改进的大量成像数据的分析。随着分辨率的提高和对全面诊断的推动，医学成像面临着新的挑战;包括更大的数据量，更长的扫描持续时间和对伪影的敏感性，如患者运动。因此，在存在系统和生理缺陷的情况下，沿着诊断能力和患者吞吐量的限制，对这些大规模数据集进行有效的处理和分析势在必行。本项目构建了一个跨学科的研究框架，提供基于几何方法的理论，算法和应用开发，以表征限制并改善医学成像重建和分析的状态。本研究包括三个互补的推力：率失真表征的学习算法;理论保证和算法的相位检索的低维模型;和优化策略的低维流形上的一类参数估计问题。每一个重点都与医学成像应用相辅相成，对美国医疗保健系统产生巨大的转化影响，包括改善医疗保健应用中的诊断和吞吐量。该项目的更广泛的教育影响来自研究与研究生和本科生课程的整合;与当地社区和弱势K-12学生的联系。

Medical imaging has benefited greatly from advances in signal and image processing, which have enabled better data acquisition, superior reconstruction and improved analysis of massive amounts of imaging data. With improving resolutions and the push for comprehensive diagnosis, medical imaging faces new challenges; including bigger data sizes, longer scan durations, and susceptibility to artifacts, such as patient motion. Hence, it is imperative that these large-scale datasets are processed and analyzed efficiently in the presence of systematic and physiological imperfections, along with constraints on diagnostic ability and patient throughput.This project builds a cross-disciplinary research framework to provide theoretical, algorithmic and application developments based on geometric methods to characterize the limits and to improve the state of medical imaging reconstruction and analysis. This research comprises three complementary thrusts: rate-distortion characterization of learning algorithms; theoretical guarantees and algorithms for phase retrieval of low-dimensional models; and optimization strategies on low-dimensional manifolds for a class of parameter estimation problems. Each of these thrusts is complemented with applications in medical imaging, with tremendous potential for translational impact in the US healthcare system, including improved diagnosis and throughput in health-care applications. Broader educational impacts of this project result from integration of the research to graduate and undergraduate curriculum; outreach to the local community and to under-privileged K-12 students.