CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising Article Swipe
Dayang Wang
,
Fenglei Fan
,
Zhan Wu
,
Rui Liu
,
Fei Wang
,
Hengyong Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acc000
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acc000
Objective . Low-dose computed tomography (LDCT) denoising is an important problem in CT research. Compared to the normal dose CT, LDCT images are subjected to severe noise and artifacts. Recently in many studies, vision transformers have shown superior feature representation ability over the convolutional neural networks (CNNs). However, unlike CNNs, the potential of vision transformers in LDCT denoising was little explored so far. Our paper aims to further explore the power of transformer for the LDCT denoising problem. Approach . In this paper, we propose a Convolution-free Token2Token Dilated Vision Transformer (CTformer) for LDCT denoising. The CTformer uses a more powerful token rearrangement to encompass local contextual information and thus avoids convolution. It also dilates and shifts feature maps to capture longer-range interaction. We interpret the CTformer by statically inspecting patterns of its internal attention maps and dynamically tracing the hierarchical attention flow with an explanatory graph. Furthermore, overlapped inference mechanism is employed to effectively eliminate the boundary artifacts that are common for encoder-decoder-based denoising models. Main results . Experimental results on Mayo dataset suggest that the CTformer outperforms the state-of-the-art denoising methods with a low computational overhead. Significance . The proposed model delivers excellent denoising performance on LDCT. Moreover, low computational cost and interpretability make the CTformer promising for clinical applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/acc000
- https://iopscience.iop.org/article/10.1088/1361-6560/acc000/pdf
- OA Status
- hybrid
- Cited By
- 180
- References
- 76
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4322622372
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4322622372Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/acc000Digital Object Identifier
- Title
-
CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoisingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-28Full publication date if available
- Authors
-
Dayang Wang, Fenglei Fan, Zhan Wu, Rui Liu, Fei Wang, Hengyong YuList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/acc000Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1361-6560/acc000/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1361-6560/acc000/pdfDirect OA link when available
- Concepts
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Noise reduction, Artificial intelligence, Computer science, Pattern recognition (psychology), Transformer, Convolutional neural network, Encoder, Inference, Computer vision, Voltage, Quantum mechanics, Physics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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180Total citation count in OpenAlex
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2025: 64, 2024: 78, 2023: 36, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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