CAREER:Development and Application of Compressive Sensing Based Interior Tomography

职业：基于压缩感知的室内层析成像技术的开发与应用

• 批准号: 1540898
• 负责人: Hengyong Yu
• 金额: $ 26.19万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1540898&HistoricalAwards=false
• 关键词: CAREER; Development; Application; Compressive; Sensing

ABSTRACT-1149679While classic computed tomography (CT) theory targets exact reconstruction of a whole cross-section or entire volume from complete projections, biomedical applications often focus on relatively small internal region-of-interests (ROIs). However, traditional CT theory cannot exactly reconstruct an internal ROI only from truncated projections associated with x-rays through the ROI because this interior problem does not have a unique solution in an unconstrained setting. In 2007, the PI and his collaborators proved that the interior problem can be exactly and stably solved if a sub-region is known inside the ROI. Inspired by the compressive sensing (CS) theory, in 2009 the PI proposed the concept of CS-based interior tomography and proved that exact interior reconstruction is achievable with an interior scan if the ROI is piecewise constant, which is subsequently extended to the case of piecewise polynomial ROI.The goal of this CAREER proposal is to advance the CS-based interior tomography theory and algorithms, and make a paradigm shift from traditional global filtered back-projection (FBP) to contemporary interior reconstruction. The three objectives are to 1) perform mathematical analysis on a general scarcity constraint model to establish uniqueness, exactness and stability, as well as the properties of the corresponding discrete scheme; 2) develop and optimize novel interior reconstruction algorithms in a general POCS framework incorporating the split-Bregman and statistical reconstruction methods; 3) verify the theoretical findings and validate the proposed algorithms via numerical simulation, and demonstrate its utility by solving the big patient problem. The research will be closely integrated with educational and outreach activities including creating a Medical Image Reconstruction course at both graduate and undergraduate levels at the Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences (SBES).

摘要-1149679虽然经典的计算机断层扫描（CT）理论的目标是从完整投影精确重建整个横截面或整个体积，但生物医学应用通常集中在相对较小的内部感兴趣区域（ROI）。然而，传统的CT理论不能仅从与通过ROI的X射线相关联的截断投影精确地重建内部ROI，因为该内部问题在无约束设置中没有唯一的解决方案。 在2007年，PI和他的合作者证明，如果ROI内部的子区域是已知的，内部问题可以精确和稳定地解决。受压缩感知（CS）理论的启发，PI于2009年提出了基于CS的内部层析成像的概念，并证明了如果ROI是分段常数，则可以通过内部扫描实现精确的内部重建，随后将其扩展到分段多项式ROI的情况。本CAREER提案的目标是推进基于CS的内部层析成像理论和算法，实现了从传统的全局滤波反投影（FBP）到现代内部重建的范式转变。本文的主要目标是：1）对一般的稀缺约束模型进行数学分析，建立其唯一性、精确性和稳定性，以及相应离散格式的性质; 2）在一般的POCS框架下，结合split-Bregman和统计重构方法，发展和优化新的内部重构算法; 3）通过数值仿真验证了理论研究结果和算法的有效性，并通过求解大病人问题验证了算法的实用性。该研究将与教育和推广活动紧密结合，包括在弗吉尼亚理工大学维克森林大学生物医学工程与科学学院（SBES）的研究生和本科生水平上创建医学图像重建课程。