Improving Limited Angle CT Reconstruction with a Robust GAN Prior

Kim, Hyojin; Thiagarajan, Jayaraman J.; Mohan, K. Aditya; Champley, Kyle

doi:10.2172/1598955

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…It is unknown how the network will perform in the presence of even higher noise levels as the radiation dose delivered to patients is further reduced. Table III contains information regarding previously published [38][39][40][41][42][43] works in the problem space of machine learningbased correction of CT image artifacts induced by incomplete sinogram acquisitions, and how our findings compare. Our method is on-par or extends on the published works in terms of dataset size, how limited the sinogram acquisition was, and extends on the metrics used for network assessment.…”

Section: Discussionmentioning

confidence: 99%

CT artifact correction for sparse and truncated projection data using generative adversarial networks

2020

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Purpose: Computed tomography image reconstruction using truncated or sparsely acquired projection data to reduce radiation dose, iodine volume, and patient motion artifacts has been widely investigated. To continue these efforts, we investigated the use of machine learning-based reconstruction techniques using deep convolutional generative adversarial networks (DCGANs) and evaluated its effect using standard imaging metrics. Methods: Ten thousand head computed tomography (CT) scans were collected from the 2019 RSNA Intracranial Hemorrhage Detection and Classification Challenge dataset. Sinograms were simulated and then resampled in both a one-third truncated and one-third sparse manner. DCGANs were tasked with correcting the incomplete projection data, either in the sinogram domain where the full sinogram was recovered by the DCGAN and then reconstructed, or the reconstruction domain where the incomplete data were first reconstructed and the sparse or truncation artifacts were corrected by the DCGAN. Seventy-five hundred images were used for network training and 2500 were withheld for network assessment using mean absolute error (MAE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) between results of different correction techniques. Image data from a quality-assurance phantom were also resampled in the two manners and corrected and reconstructed for network performance assessment using line profiles across high-contrast features, the modulation transfer function (MTF), noise power spectrum (NPS), and Hounsfield Unit (HU) linearity analysis. Results: Better agreement with the fully sampled reconstructions were achieved from sparse acquisition corrected in the sinogram domain and the truncated acquisition corrected in the reconstruction domain. MAE, SSIM, and PSNR showed quantitative improvement from the DCGAN correction techniques. HU linearity of the reconstructions was maintained by the correction techniques for the sparse and truncated acquisitions. MTF curves reached the 10% modulation cutoff frequency at 5.86 lp/cm for the truncated corrected reconstruction compared with 2.98 lp/cm for the truncated uncorrected reconstruction, and 5.36 lp/cm for the sparse corrected reconstruction compared with around 2.91 lp/cm for the sparse uncorrected reconstruction. NPS analyses yielded better agreement across a range of frequencies between the resampled corrected phantom and truth reconstructions. Conclusions: We demonstrated the use of DCGANs for CT-image correction from sparse and truncated simulated projection data, while preserving imaging quality of the fully sampled projection data.

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Section: Discussionmentioning

confidence: 99%

CT artifact correction for sparse and truncated projection data using generative adversarial networks

2020

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“…1, they firstly reconstruct the damaged Radon data into the damaged image with artifacts, then reduce the artifacts and noises in the image domain. Lots of researchers are currently using convolutional neural network (CNN) and deep learning architecture to perform this procedure [1,[5][6][7][30][31][32][33][34][35][36]. Zhang et al [30] proposed a data-driven learning method based on deep CNN.…”

Section: Iterative Reconstruction Algorithms Image Inpainting Sinogra...mentioning

confidence: 99%

Real-Time Limited-View CT Inpainting and Reconstruction with Dual Domain Based on Spatial Information

Deng,

Sun,

Liu

et al. 2021

Preprint

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Low-dose Computed Tomography is a common issue in reality. Current reduction, sparse sampling and limited-view scanning can all cause it. Between them, limited-view CT is general in the industry due to inevitable mechanical and physical limitation. However, limited-view CT can cause serious imaging problem on account of its massive information loss. Thus, we should effectively utilize the scant prior information to perform completion. It is an undeniable fact that CT imaging slices are extremely dense, which leads to high continuity between successive images. We realized that fully exploit the spatial correlation between consecutive frames can significantly improve restoration results in video inpainting. Inspired by this, we propose a deep learning-based three-stage algorithm that hoist limited-view CT imaging quality based on spatial information. In stage one, to better utilize prior information in the Radon domain, we design an adversarial autoencoder to complement the Radon data. In the second stage, a model is built to perform inpainting based on spatial continuity in the image domain. At this point, we have roughly restored the imaging, while its texture still needs to be finely repaired. Hence, we propose a model to accurately restore the image in stage three, and finally achieve an ideal inpainting result. In addition, we adopt FBP instead of SART-TV to make our algorithm more suitable for real-time use. In the experiment, we restore and reconstruct the Radon data that has been cut the rear one-third part, they achieve PSNR of 40.209, SSIM of 0.943, while precisely present the texture.

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Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior

Shu¹,

Entezari²

2022

Computer Methods and Programs in Biomedicine

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Improving Limited Angle CT Reconstruction with a Robust GAN Prior

Cited by 5 publications

References 7 publications

CT artifact correction for sparse and truncated projection data using generative adversarial networks

CT artifact correction for sparse and truncated projection data using generative adversarial networks

Real-Time Limited-View CT Inpainting and Reconstruction with Dual Domain Based on Spatial Information

Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior

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