Sparse-View CT Reconstruction via Generative Adversarial Networks

Zhao, Zhongwei; Sun, Yuewen; Cong, Peng

doi:10.1109/nssmic.2018.8824362

Cited by 15 publications

(11 citation statements)

References 4 publications

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“…Sinogram Inpainting and Image Refining algorithms are presented by yellow lines in Fig. 1, they firstly restore the missing part in the Radon domain, then reconstruct the fullview Radon data into the image domain so as to finely repair the image to obtain higher quality [8,[43][44][45][46]. In 2017, Hammernik et al [43] proposed a two-stage deep learning architecture, they first learn the compensation weights that account for the missing data in the projection domain, then they formulate the image restoration problem as a variational network to eliminate coherent streaking artifacts.…”

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

confidence: 99%

“…Zhao et al [44] proposed a GAN-based sinogram inpainting network, which achieved unsupervised training in a sinogram-imagesinogram closed loop. Zhao et al [45] also proposed a twostage method, firstly they use an interpolating convolutional network to obtain the full-view projection data, then use GAN to output high-quality CT images. In 2019, Lee et al [46] proposed a deep learning model based on fully convolutional network and wavelet transform.…”

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

confidence: 99%

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

confidence: 99%

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

View full text Add to dashboard Cite

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“…Since GAN (Generative Adversarial Networks) was designed elaborately by Goodfellow in 2014 [31], it has been adopted in many image processing tasks due to its prominent performance in realistically predicting image details. Therefore, GANs are also naturally applied to improving the quality of low-dose CT images [32,33,34]. In addition, Ye et al explored the relationship between deep learning and classical signal processing methods in [35], explained the reason why deep learning can be employed in imaging inverse problems, and provided a theoretical basis for the application of deep learning in low-dose CT reconstruction.…”

Section: Introductionmentioning

confidence: 99%

“…Since neural networks are capable of predicting unknown data in the Radon and image domains, a natural idea is to combine these two domains [48,49,34,50,51,52] to acquire better restoration results. Specifically, it first complements the Radon data, and then remove the residual artifacts and noises on images converted from the full-view Radon data.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, it first complements the Radon data, and then remove the residual artifacts and noises on images converted from the full-view Radon data. In 2018, Zhao et al proposed SVGAN [34], an artifacts reduction method for low-dose and sparse-view CT via a single model trained by GAN. In 2019, Liang et al [49] proposed a comprehensive network combining projection and image domains.…”

Section: Introductionmentioning

confidence: 99%

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A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction

Liu¹,

Deng²,

Sun³

et al. 2021

Preprint

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Sparse-view computed tomography (CT) is known as a widely used approach to reduce radiation dose while accelerating imaging through lowered projection views and correlated calculations. However, its severe imaging noise and streaking artifacts turn out to be a major issue in the low dose protocol. In this paper, we propose a dual-domain deep learning-based method that breaks through the limitations of currently prevailing algorithms that merely process single image slices. Since the scanned object usually contains a high degree of spatial continuity, the obtained consecutive imaging slices embody rich information that is largely unexplored. Therefore, we establish a cascade model named LS-AAE which aims to tackle the above problem. In addition, in order to adapt to the social trend of lightweight medical care, our model adopts the inverted residual with linear bottleneck in the module design to make it mobile and lightweight (reduce model parameters to one-eighth of its original) without sacrificing its performance. In our experiments, sparse sampling is conducted at intervals of 4°, 8°and 16°, which appears to be a challenging sparsity that few scholars have attempted. Nevertheless, our method still exhibits its robustness and achieves the state-of-the-art performance by reaching the PSNR of 40.305 and the SSIM of 0.948, while ensuring high model mobility. Particularly, it still exceeds other current methods when the sampling rate is one-fourth of them, thereby demonstrating its remarkable superiority.

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PGNet: Projection generative network for sparse‐view reconstruction of projection‐based magnetic particle imaging

Gao

et al. 2022

Medical Physics

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Background: Magnetic particle imaging (MPI) is a novel tomographic imaging modality that scans the distribution of superparamagnetic iron oxide nanoparticles. However, it is time-consuming to scan multiview two-dimensional (2D) projections for three-dimensional (3D) reconstruction in projection MPI, such as computed tomography (CT). An intuitive idea is to use the sparse-view projections for reconstruction to improve the temporal resolution. Tremendous progress has been made toward addressing the sparse-view problem in CT, because of the availability of large data sets. For the novel tomography of MPI, to the best of our knowledge, studies on the sparse-view problem have not yet been reported. Purpose: The acquisition of multiview projections for 3D MPI imaging is time-consuming. Our goal is to only acquire sparse-view projections for reconstruction to improve the 3D imaging temporal resolution of projection MPI. Methods: We propose to address the sparse-view problem in projection MPI by generating novel projections. The data set we constructed consists of three parts: simulation data set (including 3000 3D data), four phantoms data, and an in vivo mouse data. The simulation data set is used to train and validate the network, and the phantoms and in vivo mouse data are used to test the network. When the number of novel generated projections meets the requirements of filtered back projection, the streaking artifacts will be absent from MPI tomographic imaging. Specifically, we propose a projection generative network (PGNet), that combines an attention mechanism, adversarial training strategy, and a fusion loss function and can generate novel projections based on sparse-view real projections. To the best of our knowledge, we are the first to propose a deep learning method to attempt to overcome the sparse-view problem in projection MPI. Results: We compare our method with several sparse-view methods on phantoms and in vivo mouse data and validate the advantages and effectiveness of 2354

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Sparse-View CT Reconstruction via Generative Adversarial Networks

Cited by 15 publications

References 4 publications

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

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

A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction

PGNet: Projection generative network for sparse‐view reconstruction of projection‐based magnetic particle imaging

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