ArticleIEEE transactions on medical imaging2018
Penalized-Likelihood Reconstruction With High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Evaluation of Fluence Reduction versus Sparsity for Diffusion Posterior Sampling Reconstruction in Low-Dose CT.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Joint CT reconstruction of anatomy and implants using a mixed prior model.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Multi-Material Decomposition Using Spectral Diffusion Posterior Sampling.IEEE transactions on bio-medical engineering · 2025Article
- CT Reconstruction using Nonlinear Diffusion Posterior Sampling with Detector Blur Modeling.Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography · 2024Article
- X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis.Physics in medicine and biology · 2024Article
- Diffusion Posterior Sampling for Nonlinear CT Reconstruction.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction.Biomedical signal processing and control · 2023Article
- Three-material decomposition using a dual-layer flat panel detector in the presence of soft tissue motion.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Model-based three-material decomposition in dual-energy CT using the volume conservation constraint.Physics in medicine and biology · 2022Article
- Improving small animal cone beam CT resolution by mitigating x-ray focal spot induced blurring via deconvolution.Physics in medicine and biology · 2022Article
- High-resolution model-based material decomposition in dual-layer flat-panel CBCT.Medical physics · 2021Article
- Model-based dual-energy tomographic image reconstruction of objects containing known metal components.Physics in medicine and biology · 2020Article
- High-Resolution Model-based Material Decomposition for Multi-Layer Flat-Panel Detectors.Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography · 2020Article
- SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models.IEEE transactions on medical imaging · 2020Article
- Model-based Material Decomposition with System Blur Modeling.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Effect of source blur on digital breast tomosynthesis reconstruction.Medical physics · 2019Article
- Blind deconvolution in model-based iterative reconstruction for CT using a normalized sparsity measure.Physics in medicine and biology · 2019Article
- Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm.Physics in medicine and biology · 2019Article
- Predicting image properties in penalized-likelihood reconstructions of flat-panel CBCT.Medical physics · 2019Article
- X-ray imaging of a water bear offers a new look at tardigrade internal anatomy.Zoological letters · 2019Article
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7 authors.
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Abstract
We present a novel reconstruction algorithm based on a general cone-beam CT forward model, which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized-Likelihood objective function, which incorporates models of blur and correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared with deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur. In the same study, GPL-BC was able to achieve better line-pair reconstructions (in terms of segmented-image accuracy) as compared with deblurring followed by FDK, a model-based method without blur, and a model-based method with blur but not noise correlations. A prototype extremities quantitative cone-beam CT test-bench was used to image a physical sample of human trabecular bone. These data were used to compare reconstructions using the proposed method and model-based methods without blur and/or correlation to a registered CT image of the same bone sample. The GPL-BC reconstructions resulted in more accurate trabecular bone segmentation. Multiple trabecular bone metrics, including trabecular thickness (Tb.Th.) were computed for each reconstruction approach as well as the CT volume. The GPL-BC reconstruction provided the most accurate Tb.Th. measurement, 0.255 mm, as compared with the CT derived value of 0.193 mm, followed by the GPL-B reconstruction, the GPL-I reconstruction, and then the FDK reconstruction (0.271 mm, 0.309 mm, and 0.335 mm, respectively).
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