ArticleComputers in biology and medicine2018
Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation.
Article in Computers in biology and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 17 citations in OpenAlex.
- Prior Image-Guided Adaptive-Weighted Relative Total Variation for Sparse-View Computed Laminography of Plate-like Objects.Sensors (Basel, Switzerland) · 2026Article
- Multi-energy CT material decomposition using graph model improved CNN.Medical & biological engineering & computing · 2024Article
- A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images.Tomography (Ann Arbor, Mich.) · 2023Article
- Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.Quantitative imaging in medicine and surgery · 2023Article
- A multienergy computed tomography method without image segmentation or prior knowledge of X-ray spectra or materials.Heliyon · 2022Article
- Performance of Spectral Photon-Counting Coronary CT Angiography and Comparison with Energy-Integrating-Detector CT: Objective Assessment with Model Observer.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
10 authors at 6 institutions in 2 countries.
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Abstract
In this paper, we present an iterative reconstruction for photon-counting CT using prior image constrained total generalized variation (PICTGV). This work aims to exploit structural correlation in the energy domain to reduce image noise in photon-counting CT with narrow energy bins. This is motived by the fact that the similarity between high-quality full-spectrum image and target image is an important prior knowledge for photon-counting CT reconstruction. The PICTGV method is implemented using a splitting-based fast iterative shrinkage-threshold algorithm (FISTA). Evaluations conducted with simulated and real photon-counting CT data demonstrate that PICTGV method outperforms the existing prior image constrained compressed sensing (PICCS) method in terms of noise reduction, artifact suppression and resolution preservation. In the simulated head data study, the average relative root mean squared error is reduced from 2.3% in PICCS method to 1.2% in PICTGV method, and the average universal quality index increases from 0.67 in PICCS method to 0.76 in PICTGV method. The results show that the present PICTGV method improves the performance of the PICCS method for photon-counting CT reconstruction with narrow energy bins.
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