Metal Artifact Reduction for X-Ray Computed Tomography Using U-Net in Image Domain

Zhu, Linlin; Yu, Han; Li, Lei; Xi, Xiaoqi; Zhu, Mingwan; Yan, Bin

doi:10.1109/access.2019.2930302

Cited by 18 publications

(20 citation statements)

References 30 publications

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“…Existing deep MAR methods are mostly based on supervised learning, which requires the pairs of artefact-affected data and metal-free label for training, thus a distance function between network outputs and corresponding labels can be minimized. These supervised MAR methods can be divided into three categories: image-domain [3][4][5][6][7][8], sinogramdomain [9][10][11], and dual-domain [12][13][14][15][16][17]. Image domain supervised MAR methods work purely on reconstructed images and adopt techniques including residual learning [4,7], advanced network designs [5,6] like U-Net [18] and conditional GAN [19] to improve the performance.…”

Section: Introductionmentioning

confidence: 99%

“…These supervised MAR methods can be divided into three categories: image-domain [3][4][5][6][7][8], sinogramdomain [9][10][11], and dual-domain [12][13][14][15][16][17]. Image domain supervised MAR methods work purely on reconstructed images and adopt techniques including residual learning [4,7], advanced network designs [5,6] like U-Net [18] and conditional GAN [19] to improve the performance. Sinogram-domain deep MAR methods learn to reduce artefacts by processing the projections, where the corrupted signals inside metal traces are rectified.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods

Liang²,

Liu³

et al. 2021

Preprint

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Metal artefacts in CT images may disrupt image quality and interfere with diagnosis. Recently many deep-learning-based CT metal artefact reduction (MAR) methods have been proposed. Current deep MAR methods may be troubled with domain gap problem, where methods trained on simulated data cannot perform well on practical data. In this work, we experimentally investigate two image-domain supervised methods, two dual-domain supervised methods and two image-domain unsupervised methods on a dental dataset and a torso dataset, to explore whether domain gap problem exists or is overcome. We find that I-DL-MAR and DudoNet are effective for practical data of the torso dataset, indicating the domain gap problem is solved. However, none of the investigated methods perform satisfactorily on practical data of the dental dataset. Based on the experimental results, we further analyze the causes of domain gap problem for each method and dataset, which may be beneficial for improving existing methods or designing new ones. The findings suggest that the domain gap problem in deep MAR methods remains to be addressed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods

Liang²,

Liu³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…[21], a review was provided for the state-of-art technologies in metal artifact reduction, and the limitations of these technologies were also pointed out. Most recently, machine leaning methods are explored to battle the metal artifacts in CT [22][23][24][25]. In ref.…”

Section: Introductionmentioning

confidence: 99%

“…In the second category [21][22][23][24][25], the beam hardening effects are "learned" from a large set of measurements with and without metals. The learned model is automatically achieved after the training phase.…”

Section: Introductionmentioning

confidence: 99%

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Projection-domain iteration to estimate unreliable measurements

Zeng

2020

Vis. Comput. Ind. Biomed. Art

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Due to the beam-hardening effect of the broad energy spectrum of the X-ray source in computed tomography, the reconstructed images usually suffer from severe artifacts when metallic objects are being imaged. Metal artifact correction methods are usually sophisticated and not practical, especially in some non-medical applications, in which the linear attenuation coefficients are unknown. This paper suggests a simple and effective algorithm to estimate the unreliable measurements. The proposed algorithm is an iterative algorithm, in which the iteration is performed in the projection domain, while the objective function is set up in the image domain. The final image is reconstructed with the conventional filtered backprojection algorithm. The feasibility of the proposed method is verified with airport bags that contain some unknown metals.

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A semi‐supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction

et al. 2022

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Purpose: X-ray computed tomography (CT) has become a convenient and efficient clinical medical technique. However, in the presence of metal implants, CT images may be corrupted by metal artifacts. The metal artifact reduction (MAR) methods based on deep learning are mostly supervised methods trained with labeled synthetic-artifact CT images. However, this causes the neural network to be biased toward learning specific synthetic-artifact patterns and leads to a poor generalization for unlabeled real-artifact CT images. In this study, a semisupervised learning method of latent features based on convolutional neural networks (SLF-CNN) is developed to remove metal artifacts while ensuring a good generalization ability for real-artifact CT images. Methods: The proposed semi-supervised method extracts CT image features in alternate iterations of a synthetic-artifact learning stage and a real-artifact learning stage. In the synthetic-artifact learning stage, SLF-CNN is fed with paired synthetic-artifact CT images and is constrained using mean-squarederror (MSE) loss and perceptual loss in a supervised learning fashion. In the real-artifact learning stage, the network weight is updated by minimizing the error between the pseudo-ground truths and the predicted latent features. The feature level pseudo-ground truths are obtained by modeling latent features using the Gaussian process. The overall framework of SLF-CNN adopts an encoder-decoder structure. The encoder is composed of artifact information collection groups to map the input artifact-affected synthetic-artifact CT images and real-artifact CT images into latent features. The decoder is composed of stacked ResNeXt blocks and is responsible for decoding latent features with high-level semantic information to reconstruct artifact-free CT images. The performance of the proposed method is evaluated through contrast experiments and ablation experiments. Results: The contrast experimental results indicate that the artifact-free CT images obtained by SLF-CNN have good metrics values, which are close to or better than those of typical supervised MAR methods. The metal artifacts in artifact-affected CT images are eliminated and the tissue structure details are preserved using SLF-CNN. The ablation experiment shows that adding realartifact CT images greatly improves the generalization ability of the network. Conclusions: The proposed semi-supervised learning method of latent features for MAR effectively suppresses metal artifacts and improves the generalization ability of the network.

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Metal Artifact Reduction for X-Ray Computed Tomography Using U-Net in Image Domain

Cited by 18 publications

References 30 publications

Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods

Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods

Projection-domain iteration to estimate unreliable measurements

A semi‐supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction

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