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

Peng, Chengtao; Qiu, Bensheng; Li, Ming; Yang, Yang; Zhang, Cheng; Gong, Lun; Zheng, Jian

doi:10.1109/trpms.2017.2776970

Cited by 9 publications

(5 citation statements)

References 41 publications

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“…These images are implanted with metals and are projected onto CT sinograms using the FP algorithm; the attenuation coefficient of the implanted metals is set to 7.874 according to the National Institute of Standard and Technology (NIST) standard. Metal‐corrupted CT sinograms are synthesized using the dynamic wavelet thresholding method for x‐ray CT metal artifact reduction by Peng et al 19 and the dual domain network method for CT metal artifact reduction by Lin et al 31 In addition, to test the clinical efficacy of the proposed method, we evaluate it on a real clinical data (a CT image that contains real metal artifacts), which is shared by the authors of another MAR study 9 . The size of the clinical CT image is 512 × 512.…”

Section: Methodsmentioning

confidence: 99%

“…Classical iterative reconstruction MAR methods include algebraic reconstruction techniques (ART), [10][11][12] simultaneous ART (SART), [13][14][15] and statistic iterative reconstruction (SIR). [16][17][18][19] These methods used only uncorrupted sinogram pixels for CT image reconstruction, and did not need to deal with metal-corrupted areas. However, such methods are very time consuming compared to inpainting-based approaches because they need to traverse all the valid sinogram pixels (uncorrupted pixels) in each iteration and they generally take dozens of iterations to reconstruct an image.…”

Section: Introductionmentioning

confidence: 99%

“…(b) Iterative reconstruction MAR: Generally, due to oversampling of the CT system, CT images can be completely reconstructed from the uncorrupted regions of a sinogram. Classical iterative reconstruction MAR methods include algebraic reconstruction techniques (ART), 10–12 simultaneous ART (SART), 13‐15 and statistic iterative reconstruction (SIR) 16–19 . These methods used only uncorrupted sinogram pixels for CT image reconstruction, and did not need to deal with metal‐corrupted areas.…”

Section: Introductionmentioning

confidence: 99%

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An irregular metal trace inpainting network for x‐ray CT metal artifact reduction

Peng

et al. 2020

Medical Physics

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Purpose: Metal implants in the patient's body can generate severe metal artifacts in x-ray computed tomography (CT) images. These artifacts may cover the tissues around the metal implants in CT images and even corrupt the tissue regions, thus affecting disease diagnosis using these images. Previous deep learning metal trace inpainting methods used both valid pixels of uncorrupted areas and invalid pixels of corrupted areas to patch metal trace (i.e., the holes of removed metal-corrupted regions). Such methods cannot recover fine details well and often suffer information mismatch due to interference of invalid pixels, thus incurring considerable secondary artifacts. In this paper, we develop a new irregular metal trace inpainting network for reducing metal artifacts. Methods: We develop a new deep learning network to patch irregular metal trace in metal-corrupted sinograms to reduce metal artifacts for isometric fan-beam CT. Our new method patches irregular metal trace in CT sinograms using only valid pixels, avoiding interference from invalid pixels. Furthermore, to enable the inpainting network to recover as many details as possible, we design an auxiliary inpainting network to suppress the probable secondary artifacts in CT images to assist fine detail restoration. The image produced by the auxiliary network is then projected onto a sinogram via a forward projection (FP) algorithm and is fused with the sinogram predicted by the inpainting network in order to predict the final recovered sinogram. Our entire network is trained end-to-end to extract cross-domain information between the sinogram domain and CT image domain. Results: We compare our proposed method with two traditional and four deep learning-based metal trace inpainting methods, and with an iterative reconstruction method on four datasets: dental fillings (panoramic and local perspectives), hip prostheses, and spine fixations. We use both quantitative and qualitative indices to evaluate our method, and the analyses suggest that our method reduces the most metal artifacts and produces the best quality CT images. Additionally, our proposed method takes 0.1512 s on average to process a CT slice, which meets the clinical requirement. Conclusions: This paper proposes a new deep learning network to patch irregular metal trace in corrupted sinograms to reduce metal artifacts. Our method restores more fine details in irregular metal trace and has a superior capability on metal artifact reduction compared with state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An irregular metal trace inpainting network for x‐ray CT metal artifact reduction

Peng

et al. 2020

Medical Physics

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“…In continue, some of these methods are reviewed. A dynamic wavelet thresholding metal artifact reduction algorithm was produced by Peng et al [17], which is based on a statistic iterative reconstruction model for CT images. For removing blocky black artifacts caused by incomplete projections, cubic spline interpolation was utilized.…”

Section: Literature Reviewmentioning

confidence: 99%

Reduction of Metal Artifact in Dental Computed Tomography by Homomorphic Wavelet Filtering in Sinogram Domain

Hassanpour

Ebadi

Zehtabian

et al. 2018

IJEE

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In X-ray computed tomography (CT), existence of metallic implants in the subject may corrupt images and produce dark and bright streaking artifacts. In this paper a new method for reducing metal artifact from dental X-ray CT images is introduced. In the proposed method, the Radon transform is used in order to project the CT data into the sinogram domain. The sinogram of data can be decomposed into its illumination and reflectance components by using the homomorphic wavelet filtering. The investigation of the CT images shows that the degradations caused by metallic artifacts appear mainly in the illumination component. Therefore, in our approach the corrupted illumination component is restored by using the apriori information driven from the previous artifact-free sections. The results showed that the metal artifacts are considerably reduced without eliminating the important details of the CT images. The proposed method is also compared with other existing methods on a set of dental CT images. Comparisons showed the superiority of the proposed method over other existing methods.

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“…Classical iterative reconstruction MAR methods can be divided into two groups. One uses projection data outside of the metal trace, which can be regarded as clean data [11,[17][18][19][20][21][22]. The other adopts a statistical objective function to decay corrupted projection data [23].…”

Section: Introductionmentioning

confidence: 99%

DAN-Net: Dual-domain adaptive-scaling non-local network for CT metal artifact reduction

et al. 2021

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Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and negatively impact subsequent diagnoses and treatment planning. With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT. Despite the encouraging results achieved by these methods, there is still much room to further improve performance. In this paper, a novel Dual-domain Adaptive-scaling Non-local network (DAN-Net) for MAR. We correct the corrupted sinogram using adaptive scaling first to preserve more tissue and bone details as a more informative input. Then, an end-to-end dual-domain network is adopted to successively process the sinogram and its corresponding reconstructed image generated by the analytical reconstruction layer. In addition, to better suppress the existing artifacts and restrain the potential secondary artifacts caused by inaccurate results of the sinogram-domain network, a novel residual sinogram learning strategy and nonlocal module are leveraged in the proposed network model. In the experiments, the proposed DAN-Net demonstrates performance competitive with several state-of-the-art MAR methods in both qualitative and quantitative aspects.

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

Cited by 9 publications

References 41 publications

An irregular metal trace inpainting network for x‐ray CT metal artifact reduction

An irregular metal trace inpainting network for x‐ray CT metal artifact reduction

Reduction of Metal Artifact in Dental Computed Tomography by Homomorphic Wavelet Filtering in Sinogram Domain

DAN-Net: Dual-domain adaptive-scaling non-local network for CT metal artifact reduction

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