Poster + Paper
7 April 2023 Accurate image reconstruction in dual-energy CT with limited-angular-range data from completely non-overlapping scanning arcs
Author Affiliations +
Conference Poster
Abstract
Dual-energy CT (DECT) with limited-angular-range (LAR) data has the potential to reduce radiation dose, scanning time, and motion effect, and avoid collision between the moving gantry and the patient. There are two primary sources of image artifacts, LAR and beam-hardening (BH) effects. Previous works have demonstrated that LAR artifacts can be effectively reduced or eliminated by the directional-total-variation (DTV) constraints on the orthogonal axes of the image array and BH artifacts can be corrected for by using the data-domain decomposition approach with overlapping scanning arcs. In this work, we investigate a one-step method for the simultaneous correction of LAR and BH artifacts for DECT with LAR data, thus enabling flexible scanning configurations, such as completely non-overlapping scanning arcs. Specifically, two scanning configurations, two-orthogonal-arc (TOA) and two-parallel-arc (TPA) configurations, are used to generate data from a digital chest phantom with low and high-kVp spectra. Basis images are reconstructed directly from low- and high-kVp data by solving a non-convex optimization problem with DTV constraints. They can then be combined into monochromatic images for visual inspection and quantitative analysis. The results suggest that accurate monochromatic images can be obtained from TOA and TPA configurations of 90◦ arcs, and that the TOA configuration appears to be more robust to data inconsistencies such as noise.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Buxin Chen, Zheng Zhang, Dan Xia, Emil Y. Sidky, and Xiaochuan Pan "Accurate image reconstruction in dual-energy CT with limited-angular-range data from completely non-overlapping scanning arcs", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 1246330 (7 April 2023); https://doi.org/10.1117/12.2654073
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KEYWORDS
Data modeling

Algorithm development

Chest

Computed tomography

Iodine

Mathematical optimization

Reconstruction algorithms

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