Department of Radiology, The University of Chicago, Chicago, IL 60637, USA.
Phys Med Biol. 2012 Aug 21;57(16):5245-73. doi: 10.1088/0031-9155/57/16/5245. Epub 2012 Jul 31.
In this work, we investigate optimization-based image reconstruction from few-view (i.e. less than ten views) projections of sparse objects such as coronary-artery specimens. Using optimization programs as a guide, we formulate constraint programs as reconstruction programs and develop algorithms to reconstruct images through solving the reconstruction programs. Characterization studies are carried out for elucidating the algorithm properties of 'convergence' (relative to designed solutions) and 'utility' (relative to desired solutions) by using simulated few-view data calculated from a discrete FORBILD coronary-artery phantom, and real few-view data acquired from a human coronary-artery specimen. Study results suggest that carefully designed reconstruction programs and algorithms can yield accurate reconstructions of sparse images from few-view projections.
在这项工作中，我们研究了从稀疏物体（如冠状动脉标本）的少数视图（即少于十视图）投影中进行基于优化的图像重建。我们使用优化程序作为指导，将约束程序制定为重建程序，并开发算法通过求解重建程序来重建图像。通过使用从离散 FORBILD 冠状动脉模型计算的模拟少数视图数据以及从人体冠状动脉标本获得的真实少数视图数据，进行特征研究，阐明了“收敛性”（相对于设计解决方案）和“实用性”（相对于所需解决方案）的算法特性。研究结果表明，精心设计的重建程序和算法可以从少数视图投影中准确重建稀疏图像。
