SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models

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 i...

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Vydané v:IEEE transactions on medical imaging Ročník 39; číslo 3; s. 729 - 741
Hlavní autori: Ye, Siqi, Ravishankar, Saiprasad, Long, Yong, Fessler, Jeffrey A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Algorithms
Clustering
Computed tomography
Computer applications
Data models
Dictionaries
efficient algorithms
Humans
Image coding
Image processing
Image Processing, Computer-Assisted - methods
Image quality
Image reconstruction
Inverse problems
Iterative methods
Likelihood Functions
Machine Learning
Mathematical models
nonconvex optimization
Poisson Distribution
Radiation Dosage
shifted-Poisson model
sparse representation
Statistical analysis
Statistical models
Tomography, X-Ray Computed - methods
transform learning
Transforms
Two dimensional displays
X-ray imaging
ISSN:0278-0062, 1558-254X, 1558-254X
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Popis
Shrnutí: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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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2019.2934933