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• W3103702404 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." @default.
• W3103702404 created "2020-11-23" @default.
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• W3103702404 date "2020-03-01" @default.
• W3103702404 modified "2023-10-14" @default.
• W3103702404 title "SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models" @default.
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• W3103702404 doi "https://doi.org/10.1109/tmi.2019.2934933" @default.
• W3103702404 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7170173" @default.
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