Suppression of metal artifact in computed tomography by reweighted total variation reconstruction

Ming, 李铭 Li; Jian, 郑健 Zheng; Cheng, 章程 Zhang; Zhe, 周哲 Zhou; Tao, 张涛 Zhang

doi:10.3788/ope.20152306.1714

Cited by 3 publications

(1 citation statement)

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“…The denoised image from DADN (Figure 10c) is closer to the background content information of the LDCT image in terms of image structure and detail. Moreover, the modulation transfer function (MTF) [49][50][51] curve that characterizes the spatial resolution of images was calculated for DADN-O and DADN. The light blue box indicated in Figure 10 provides the point spread function, and MTF can be obtained by calculating twodimensional discrete Fourier transformer.…”

Section: Effect Of Transfer Learningmentioning

confidence: 99%

Domain‐adaptive denoising network for low‐dose CT via noise estimation and transfer learning

Wang

Tang

et al. 2022

Medical Physics

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Background: In recent years, low-dose computed tomography (LDCT) has played an important role in the diagnosis CT to reduce the potential adverse effects of X-ray radiation on patients, while maintaining the same diagnostic image quality. Purpose: Deep learning (DL)-based methods have played an increasingly important role in the field of LDCT imaging. However, its performance is highly dependent on the consistency of feature distributions between training data and test data. Due to patient's breathing movements during data acquisition, the paired LDCT and normal dose CT images are difficult to obtain from realistic imaging scenarios. Moreover, LDCT images from simulation or clinical CT examination often have different feature distributions due to the pollution by different amounts and types of image noises. If a network model trained with a simulated dataset is used to directly test clinical patients' LDCT data, its denoising performance may be degraded. Based on this, we propose a novel domain-adaptive denoising network (DADN) via noise estimation and transfer learning to resolve the out-of -distribution problem in LDCT imaging. Methods: To overcome the previous adaptation issue, a novel network model consisting of a reconstruction network and a noise estimation network was designed. The noise estimation network based on a double branch structure is used for image noise extraction and adaptation. Meanwhile, the U-Net-based reconstruction network uses several spatially adaptive normalization modules to fuse multi-scale noise input. Moreover, to facilitate the adaptation of the proposed DADN network to new imaging scenarios, we set a two-stage network training plan. In the first stage, the public simulated dataset is used for training. In the second transfer training stage, we will continue to fine-tune the network model with a torso phantom dataset, while some parameters are frozen. The main reason using the two-stage training scheme is based on the fact that the feature distribution of image content from the public dataset is complex and diverse,whereas the feature distribution of noise pattern from the torso phantom dataset is closer to realistic imaging scenarios. Results: In an evaluation study, the trained DADN model is applied to both the public and clinical patient LDCT datasets. Through the comparison of visual inspection and quantitative results, it is shown that the proposed DADN network model can perform well in terms of noise and artifact suppression, while effectively preserving image contrast and details. Conclusions: In this paper, we have proposed a new DL network to overcome the domain adaptation problem in LDCT image denoising. Moreover, the results demonstrate the feasibility and effectiveness of the application of our proposed DADN network model as a new DL-based LDCT image denoising method.

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Section: Effect Of Transfer Learningmentioning

confidence: 99%

Domain‐adaptive denoising network for low‐dose CT via noise estimation and transfer learning

Wang

Tang

et al. 2022

Medical Physics

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GPU-Accelerated Dynamic Wavelet Thresholding Algorithm for X-Ray CT Metal Artifact Reduction

Peng

Qiu

et al. 2018

IEEE Trans. Radiat. Plasma Med. Sci.

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Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression

Cui,

Lv,

Wang

et al. 2024

Journal of X-Ray Science and Technology: Clinical Applications

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BACKGROUND: Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. OBJECTIVE: To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. METHODS: Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. RESULTS: We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. CONCLUSIONS: In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.

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Suppression of metal artifact in computed tomography by reweighted total variation reconstruction

Cited by 3 publications

References 0 publications

Domain‐adaptive denoising network for low‐dose CT via noise estimation and transfer learning

Domain‐adaptive denoising network for low‐dose CT via noise estimation and transfer learning

GPU-Accelerated Dynamic Wavelet Thresholding Algorithm for X-Ray CT Metal Artifact Reduction

Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression

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