ArticleProceedings of SPIE--the International Society for Optical Engineering2019
Local response prediction in model-based CT material decomposition.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Design of Multiple Focal Spots for High-Resolution CT.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Design Optimization of Spatial-Spectral Filters for Cone-Beam CT Material Decomposition.IEEE transactions on medical imaging · 2022Article
- A prototype spatial-spectral CT system for material decomposition with energy-integrating detectors.Medical physics · 2021Article
- Perturbation Response of Model-based Material Decomposition with Edge-Preserving Penalties.Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography · 2020Article
- Prospective Prediction and Control of Image Properties in Model-based Material Decomposition for Spectral CT.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Combining Spectral CT Acquisition Methods for High-Sensitivity Material Decomposition.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Spectral CT is an emerging modality that permits material decomposition and density estimation through the use of energy-dependent information in measurements. Direct model-based material decomposition algorithms have been developed that incorporate statistical models and advanced regularization schemes to improve density estimates and lower exposure requirements. However, understanding and control of the relationship between regularization and image properties is complex with interactions between spectral channels and material bases. In particular, regularization in one material basis can affect the image properties of other material bases, and vice versa. In this work, we derived a closed-form set of local impulse responses for the solutions to a general, regularized, model-based material decomposition (MBMD) objective. These predictors quantify both the spatial resolution in each material image as well as the influence of regularization of one material basis on other material images. This information can be used prospectively to tune regularization parameters for specific imaging goals.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.