| 1. |
- Xie, Shipeng, et al.
(författare)
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Artifact Removal Using GAN Network for Limited-Angle CT Reconstruction
- 2019
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Ingår i: 2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019.
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Konferensbidrag (refereegranskat)abstract
- Computed tomography (CT) plays an increasingly important role in clinical diagnosis. However, in practical applications of CT, physical limitations on acquisition lead to some blind regions where data cannot be sampled. CT image reconstruction from limited-angle would enable a rapid scanning with a reduced x-ray dose delivered to the patient. As it is known, Generative Adversarial Networks (GAN) can keep the original information and details of the sample very well. In this paper, we propose an end-to-end Generative Adversarial Networks model used for removing artifacts from limited-angle CT reconstruction images. The proposed GAN is based on the conditional GAN with the joint loss function, which .can remove the artifacts while retaining the complete details and sharp edges. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. Compared to several other classic methods, our GAN model shows better consequent, in terms of artifact reduction, feature preservation, and computational efficiency for limited-angle CT reconstruction.
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| 2. |
- Xie, Shipeng, et al.
(författare)
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Non-Blind Image Deblurring Method by the Total Variation Deep Network
- 2019
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Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 37536-37544
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Tidskriftsartikel (refereegranskat)abstract
- There are a lot of non-blind image deblurring methods, especially with the total variation (TV) model-based method. However, how to choose the parameters adaptively for regularization is a major open problem. We proposed a very novel method that is based on the TV deep network to learn the best parameters adaptively for regularization. We used deep learning and prior knowledge to set up a TV-based deep network and calculate the parameters of regularization, such as biases and weights. Therefore, we used the idea of a deep network to update these parameters automatically to avoid sophisticated calculations. Our experimental results by our proposed network are significantly better than several other methods, in respect of detail retention and anti-noise performance. At the same time, we can achieve the same effect with a minimum number of training sets, thus speeding up the calculation.
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| 3. |
- Xie, Shipeng, et al.
(författare)
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Scatter Artifacts Removal Usings Using Learning-Based Method for CBCT in IGRT System
- 2018
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Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 6, s. 78031-78037
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Tidskriftsartikel (refereegranskat)abstract
- Cone-beam-computed tomography (CBCT) has shown enormous potential in recent years, but it is limited by severe scatter artifacts. This paper proposes a scatter-correction algorithm based on a deep convolutional neural network to reduce artifacts for CBCT in an image-guided radiation therapy (IGRT) system. A two-step registration method that is essential in our algorithm is implemented to preprocess data before training. The testing result on real data acquired from the IGRT system demonstrates the ability of our approach to learn artifacts distribution. Furthermore, the proposed method can be applied to enhance the performance on such applications as dose estimation and segmentation.
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