ArticleIEEE transactions on medical imaging2020
SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models.
Article in IEEE transactions on medical imaging, 2020. 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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Who cites it
6 citing papers in PubMed.
- A pre-log correction method based on dynamic approximation to reduce photon-starved deterioration.Physical and engineering sciences in medicine · 2026Article
- Existence, uniqueness, and efficiency of numerically unbiased attenuation pathlength estimators for photon counting detectors at low count rates.Medical physics · 2024Article
- Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CT.IEEE transactions on medical imaging · 2023Article
- Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning.Medical physics · 2022Article
- Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship.Medical physics · 2020Article
- Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
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4 authors.
Funding
Abstract
Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-least squares (PWLS). Due to the underlying limitations of the post-log statistical model, the PWLS reconstruction quality is often degraded in low-dose scans. This paper investigates a shifted-Poisson (SP) model based likelihood function that uses the pre-log raw measurements that better represents the measurement statistics, together with a data-driven regularizer exploiting a Union of Learned TRAnsforms (SPULTRA). Both the SP induced data-fidelity term and the regularizer in the proposed framework are nonconvex. The proposed SPULTRA algorithm uses quadratic surrogate functions for the SP induced data-fidelity term. Each iteration involves a quadratic subproblem for updating the image, and a sparse coding and clustering subproblem that has a closed-form solution. The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
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