A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution
文献类型：期刊论文
| 作者 | Xiaokun Liang; Yaoqin Xie; Guohua Cao; Zhicheng Zhang; Xu Dong |
| 刊名 | IEEE Transaction On Medical Imaging
 |
| 出版日期 | 2018 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14243]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Xiaokun Liang,Yaoqin Xie,Guohua Cao,et al. A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution[J]. IEEE Transaction On Medical Imaging,2018. |
| APA | Xiaokun Liang,Yaoqin Xie,Guohua Cao,Zhicheng Zhang,&Xu Dong.(2018).A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution.IEEE Transaction On Medical Imaging. |
| MLA | Xiaokun Liang,et al."A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution".IEEE Transaction On Medical Imaging (2018). |
入库方式： OAI收割
来源：深圳先进技术研究院
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