Adversarial Sparse-View CBCT Artifact Reduction

Liao, Haofu; Huo, Zhimin; Sehnert, William J.; Zhou, S. Kevin; Luo, Jiebo

doi:10.1007/978-3-030-00928-1_18

Cited by 22 publications

(12 citation statements)

References 19 publications

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“…A conditional GAN is used for the reduction of metal artifacts (RMA) in computed tomography (CT) ear images of cochlear implants (CIs) recipients in [291], and to estimate the high-quality full-dose PET images from low-dose ones in [292]. To reduce the artifacts from sparsely reconstructed cone-beam CT (CBCT) images, Liao et al [293] introduced a least squares generative adversarial networks (LSGAN), which is formulated under an image-to-image generative model. Chen et al [294] introduced a 3D neural network design, namely a multi-level densely connected super-resolution network (mDCSRN) with a GAN to generate a high-resolution (HR) magnetic resonance images (MRI) from one single low-resolution (LR) input image.…”

Section: Emergent Architectures: the Generative Adversarial Networkmentioning

confidence: 99%

Deep Learning in the Biomedical Applications: Recent and Future Status

2019

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Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

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Section: Emergent Architectures: the Generative Adversarial Networkmentioning

confidence: 99%

Deep Learning in the Biomedical Applications: Recent and Future Status

2019

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“…Jin et al [13] and Chen et al [14] proposed to use UNet [15] and Residual UNet to post-process the noise/artifacts in the sparse-view CT. In [16] and [17], adversarial loss and perceptual loss were used to reinforce the network's learning. Later, Zhang et al [18] and Han et al [19] proposed to incorporate dense block and wavelet decomposition into UNet for more robust feature learning for reconstruction.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the projection data fidelity constraint of unmeasured projection data is not incorporated in the network design and used only in the separated test stage. On a different note, the network design issue is highly under-explored as a research topic and still limited to UNetbased or auto-encoder architectures [13], [14], [16], [17], [19], [20], [22]. In addition, none of previous works have evaluated the performance under both LA and SV scenarios, and reconstruction evaluation on CT scan with pathological finding are barely performed.…”

Section: Introductionmentioning

confidence: 99%

Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer

Zhou

Duncan

et al. 2021

IEEE Trans. Med. Imaging

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Limited view tomographic reconstruction aims to reconstruct a tomographic image from a limited number of projection views arising from sparse view or limited angle acquisitions that reduce radiation dose or shorten scanning time. However, such a reconstruction suffers from severe artifacts due to the incompleteness of sinogram. To derive quality reconstruction, previous methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction. In this work, we propose a cascaded residual dense spatial-channel attention network consisting of residual dense spatial-channel attention networks and projection data fidelity layers. We evaluate our methods on two datasets. Our experimental results on AAPM Low Dose CT Grand Challenge datasets demonstrate that our algorithm achieves a consistent and substantial improvement over the existing neural network methods on both limited angle reconstruction and sparse view reconstruction. In addition, our experimental results on Deep Lesion datasets demonstrate that our method is able to generate high-quality reconstruction for 8 major lesion types.

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“…With the same Wasserstein adversarial loss and perceptual loss, Shan et al [45] proposed a 2D Conveying Path-based Convolutional Encoder-decoder (CPCE) network and entended it to 3D model, resulting in better performance in noise suppression and structure preservation. Liao et al [46] incorporated a feature pyramid network (FPN)-based discriminator and a differentially modulated focus map to the least squares GAN (LSGAN) [47], outperforming other methods in correcting cone-beam artifacts in the image. Yi et al [48] introduced a sharpness loss in addition to adversarial loss and perceptual loss to ensure the final sharpness of the image and the faithful reconstruction of low-contrast regions.…”

Section: Introductionmentioning

confidence: 99%

Single Low-Dose CT Image Denoising Using a Generative Adversarial Network With Modified U-Net Generator and Multi-Level Discriminator

Chi

et al. 2020

IEEE Access

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Low-dose CT (LDCT) images have been widely applied in the medical imaging field due to the potential risk of exposing patients to X-ray radiations. Given the fact that reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures or false lesions derived from noise. In this paper, we propose a generative adversarial network (GAN) with novel architecture and loss function for restoring the LDCT image. Firstly, the inception-residual block and residual mapping are incoporated in the U-Net structure. The modified U-Net is applied as the generator of the GAN network so that the noise feature can be eliminated during the forward propagation. Secondly, a novel multi-level joint discriminator is designed by concatenating multiple convolutional neural networks (CNNs) where the output of each deconvolutional layer in the generator is compared with the corresponding down-sampled ground truth image. The adversarial training can be sensitive to noise and artifacts in different scales with this discriminator. Thirdly, we novely define a loss function consisting of the least square adversarial loss, VGG based perceptual loss, MSE based pixel loss and the noise loss, so that the differences in pixel, visual perception and noise distribution are comprehensively considered to optimize the network. Experimental results on both simulated and official simulated clinical images have demonstrated that the proposed method can provide superior performance to the state-of-the-art methods in noise removal, structure preservation and false lesions elimination. INDEX TERMS Low-dose CT image denoising, deep learning, generative adversarial network, inception block, residual mapping, joint loss.

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Adversarial Sparse-View CBCT Artifact Reduction

Cited by 22 publications

References 19 publications

Deep Learning in the Biomedical Applications: Recent and Future Status

Deep Learning in the Biomedical Applications: Recent and Future Status

Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer

Single Low-Dose CT Image Denoising Using a Generative Adversarial Network With Modified U-Net Generator and Multi-Level Discriminator

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