Limited-View Cone-Beam CT Reconstruction Based on an Adversarial Autoencoder Network With Joint Loss

Dai, Xiubin; Bai, Jianan; Liu, Tianliang; Xie, Lizhe

doi:10.1109/access.2018.2890135

Cited by 16 publications

(9 citation statements)

References 36 publications

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“…The use of DL techniques for CT reconstruction has predominantly involved sparse‐view and low‐dose acquisitions as there is no ideal tomographic ground truth for learning strategies 105–110 . Iterative reconstruction (IR) applications utilizing DL can be categorized into “plug‐and‐play” and “unrolling” methods 109 .…”

Section: Discussionmentioning

confidence: 99%

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

et al. 2022

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The use of deep learning (DL) to improve cone‐beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up‐to‐date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone‐beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings—with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state‐of‐the‐art methods utilized in radiation oncology.

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Section: Discussionmentioning

confidence: 99%

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

et al. 2022

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“…Sinogram Inpainting algorithms are presented by green lines in Fig. 1, they firstly restore the missing part in the Radon domain, then reconstruct it into the image domain to get the final result [37][38][39][40][41]. Li et al [37] proposed an effective GAN-based repairing method named patch-GAN, which trains the network to learn the data distribution of the sinogram to restore the missing sinogram data.…”

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

confidence: 99%

“…In another paper [38], Li et al proposed SI-GAN on the basis of [32], using a joint loss function combining the Radon domain and the image domain to repair "ultra-limited-angle" sinogram. In 2019, Dai et al [39] proposed a limited-view cone-beam CT reconstruction algorithm. It slices the conebeam projection data into the sequence of two-dimensional images, uses an autoencoder network to estimate the missing part, then stack them in order and finally use FDK [42] for three-dimensional reconstruction.…”

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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“…Much work has been done on deep learning-based reconstructions using sparse-view measurements for CT, PAT, and OPT. With the help of deep learning networks, such as U-net and GAN, high-quality images with a much clearer edge and fine structural information can be obtained using sparse-view data, thus achieving faster imaging speeds (37)(38)(39)(40)(46)(47)(48)(49)(50). For example, Davis et al used U-net to remove artifacts, reducing the processing time for an undersampled OPT dataset.…”

Section: Introductionmentioning

confidence: 99%

Two-stage deep learning network-based few-view image reconstruction for parallel-beam projection tomography

Wang¹,

Wang²,

Xie³

et al. 2022

Quant Imaging Med Surg

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Background: Projection tomography (PT) is a very important and valuable method for fast volumetric imaging with isotropic spatial resolution. Sparse-view or limited-angle reconstruction-based PT can greatly reduce data acquisition time, lower radiation doses, and simplify sample fixation modes. However, few techniques can currently achieve image reconstruction based on few-view projection data, which is especially important for in vivo PT in living organisms.Methods: A 2-stage deep learning network (TSDLN)-based framework was proposed for parallel-beam PT reconstructions using few-view projections. The framework is composed of a reconstruction network (R-net) and a correction network (C-net). The R-net is a generative adversarial network (GAN) used to complete image information with direct back-projection (BP) of a sparse signal, bringing the reconstructed image close to reconstruction results obtained from fully projected data. The C-net is a U-net array that denoises the compensation result to obtain a high-quality reconstructed image. Results:The accuracy and feasibility of the proposed TSDLN-based framework in few-view projectionbased reconstruction were first evaluated with simulations, using images from the DeepLesion public dataset. The framework exhibited better reconstruction performance than traditional analytic reconstruction algorithms and iterative algorithms, especially in cases using sparse-view projection images. For example, with as few as two projections, the TSDLN-based framework reconstructed high-quality images very close to the original image, with structural similarities greater than 0.8. By using previously acquired optical PT (OPT) data in the TSDLN-based framework trained on computed tomography (CT) data, we further exemplified the migration capabilities of the TSDLN-based framework. The results showed that when the number of projections was reduced to 5, the contours and distribution information of the samples in question could still be seen in the reconstructed images. Conclusions:The simulations and experimental results showed that the TSDLN-based framework has strong reconstruction abilities using few-view projection images, and has great potential in the application of in vivo PT.

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Limited-View Cone-Beam CT Reconstruction Based on an Adversarial Autoencoder Network With Joint Loss

Cited by 16 publications

References 36 publications

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

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

Two-stage deep learning network-based few-view image reconstruction for parallel-beam projection tomography

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