Premium
Image reconstruction in peripheral and central regions‐of‐interest and data redundancy
Author(s) -
Pan Xiaochuan,
Zou Yu,
Xia Dan
Publication year - 2005
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1844171
Subject(s) - beam (structure) , iterative reconstruction , computer vision , redundancy (engineering) , computer science , region of interest , artificial intelligence , image (mathematics) , algorithm , optics , physics , operating system
Algorithms have been developed for image reconstruction within a region‐of‐interest (ROI) from fan‐beam data less than that required for reconstructing the entire image. However, these algorithms do not admit truncated data. In this work, we investigate exact ROI‐image reconstruction from fan‐beam data containing truncations by use of the so‐called fan‐beam backprojection‐filtration (BPF) algorithm. We also generalize the fan‐beam BPF algorithm to exploit redundant information inherent in the truncated fan‐beam data. Because the parallel‐beam scan can be interpreted as a special case of the fan‐beam scan, based upon the fan‐beam BPF algorithm, we derive a parallel‐beam BPF algorithm for exactly reconstructing ROI images from truncated parallel‐beam data. Furthermore, we investigate image reconstruction within two types of distinctive ROIs, which are referred to as the peripheral and central ROIs, respectively, from fan‐beam data containing truncations and discuss their potential clinical applications. The results can readily be generalized to reconstructing 3D ROI images from data acquired in circular and helical cone‐beam scan. They can also be extended to address ROI‐image‐reconstruction problems in parallel‐, fan‐, and cone‐beam scans with general trajectories. The work not only has significant implications for clinical and animal‐imaging applications of CT, but also may find applications in other imaging modalities.