Low-count PET image reconstruction based on truncated inverse radon layer and U-shaped network Article Swipe
Jianbo Ye
,
Zhonghua Kuang
,
Yongfeng Yang
,
Ke Cui
,
Xiangyu Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ace240
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ace240
Objective. Positron emission tomography (PET) is a functional imaging widely used in various applications such as tumour detection. PET image reconstruction is an ill-posed inverse problem, and the model-based iterative reconstruction methods commonly used in clinical practice have disadvantages such as long time consumption and low signal-to-noise ratio, especially at low doses. Approach. In this study, we propose a deep learning-based reconstruction method that is capable of reconstructing images directly from low-count sinograms. Our network consists of two parts, a truncated inverse radon layer for implementing domain transform and a U-shaped network for image enhancement. Main result. We validated our method on both simulation data and real data. Compared to ordered subset expectation maximization with a post-Guassian filter, the structural similarity can be improved from 0.9357 to 0.9613 and the peak signal-to-noise ratio can be improved by 5 dB. Significance. The proposed method can directly convert low-count sinograms into PET images, while obtaining improved image quality and having less time consumption than iterative reconstruction algorithms and the state-of-the-art convolutional neural network.
Related Topics
Concepts
Iterative reconstruction
Computer science
Convolutional neural network
Image quality
Peak signal-to-noise ratio
Radon transform
Similarity (geometry)
Artificial intelligence
Algorithm
Noise (video)
Expectation–maximization algorithm
Projection (relational algebra)
Signal-to-noise ratio (imaging)
Noise reduction
Filter (signal processing)
Iterative method
Deep learning
Inverse
Image (mathematics)
Computer vision
Mathematics
Statistics
Maximum likelihood
Telecommunications
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/ace240
- OA Status
- hybrid
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382199181
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382199181Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/ace240Digital Object Identifier
- Title
-
Low-count PET image reconstruction based on truncated inverse radon layer and U-shaped networkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-27Full publication date if available
- Authors
-
Jianbo Ye, Zhonghua Kuang, Yongfeng Yang, Ke Cui, Xiangyu LiList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/ace240Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1361-6560/ace240Direct OA link when available
- Concepts
-
Iterative reconstruction, Computer science, Convolutional neural network, Image quality, Peak signal-to-noise ratio, Radon transform, Similarity (geometry), Artificial intelligence, Algorithm, Noise (video), Expectation–maximization algorithm, Projection (relational algebra), Signal-to-noise ratio (imaging), Noise reduction, Filter (signal processing), Iterative method, Deep learning, Inverse, Image (mathematics), Computer vision, Mathematics, Statistics, Maximum likelihood, Telecommunications, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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29Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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