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DuDoNet: Dual Domain Network for CT Metal Artifact Reduction
Wei-An Lin,Haofu Liao,Cheng Peng,Xiaohang Sun,Jingdan Zhang,Jiebo Luo,Rama Chellappa,Shaohua Kevin Zhou +7 more
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TLDR
This work proposes an end-to-end trainable Dual Domain Network (DuDoNet) to simultaneously restore sinogram consistency and enhance CT images, and is the first end- to-end dual-domain network for MAR.Abstract:
Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which creates the problem of metal artifact reduction (MAR). Existing methods for reducing the artifacts due to metallic implants are inadequate for two main reasons. First, metal artifacts are structured and non-local so that simple image domain enhancement approaches would not suffice. Second, the MAR approaches which attempt to reduce metal artifacts in the X-ray projection (sinogram) domain inevitably lead to severe secondary artifact due to sinogram inconsistency. To overcome these difficulties, we propose an end-to-end trainable Dual Domain Network (DuDoNet) to simultaneously restore sinogram consistency and enhance CT images. The linkage between the sigogram and image domains is a novel Radon inversion layer that allows the gradients to back-propagate from the image domain to the sinogram domain during training. Extensive experiments show that our method achieves significant improvements over other single domain MAR approaches. To the best of our knowledge, it is the first end-to-end dual-domain network for MAR.read more
Citations
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Journal ArticleDOI
DIOR: Deep Iterative Optimization-Based Residual-Learning for Limited-Angle CT Reconstruction
TL;DR: Compared with existing competitive algorithms, quantitative and qualitative results show that the proposed DIOR brings a promising improvement in artifact removal, detail restoration and edge preservation.
Journal ArticleDOI
DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography.
Bo Zhou,Disha Yadav, Dr. Ramesh C Devrath, Dr. Gyanendra B. S. Johri,Xiongchao Chen,S. Kevin Zhou,James S. Duncan,Chi Liu +5 more
TL;DR: DuDoDR-Net as discussed by the authors proposes a dual-domain data consistent recurrent network for SVMAR, which can reconstruct an artifact-free image by recurrent image domain and sinogram domain restorations.
Book ChapterDOI
U-DuDoNet: Unpaired Dual-Domain Network for CT Metal Artifact Reduction
TL;DR: U-DuDoNet as mentioned in this paper combines the advantages of both supervised and unsupervised deep learning methods on the CT metal artifact reduction (MAR) task, and proposes a self-learned sinogram prior net, which provides guidance for restoring the information in the sinogram domain, and cyclic constraints for artifact reduction and addition.
Journal ArticleDOI
A Review of the Methods on Cobb Angle Measurements for Spinal Curvature
TL;DR: The research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning by comparing the measurement effects of typical methods and their advantages and disadvantages.
Journal ArticleDOI
DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction
TL;DR: The experimental results demonstrate that the DuDoUFNet method can provide high-quality reconstruction, superior to previous LDCT and MAR methods under various low-dose and metal settings, as well as other CT-related applications.
References
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Proceedings ArticleDOI
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