Low-dose CT imaging via cascaded ResUnet with spectrum loss

Liu, Jin; Kang, Yanqin; Qiang, Jun; Wang, Yong; Hu, Dianlin; Chen, Yang

doi:10.1016/j.ymeth.2021.05.005

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…1, a technique for metal artifact reduction (MAR) is proposed using two different neural networks. The modified U-Net [7] model was used for interpolation in the sinogram domain, while another modified Residual U-Net (ResU-Net) [12] model handled the residual noise in the reconstructed images, as shown in Fig. 2.…”

Section: Structure Of the Proposed Methods With Deep Learningmentioning

confidence: 99%

“…2 (a). Similarly, the structure of the ResU-Net model [12] was modified in this work. It used convolution with the stride of 2 × 2 pixels for down sampling the input data, as well as the skip connection.…”

Section: Structure Of the Proposed Methods With Deep Learningmentioning

confidence: 99%

“…In this work, we proposed a method that can alleviate streak and metal artifacts on reconstructed images using deep learning. Two neural network models [7,11,12] were used to improve image quality in both sinogram and image domains. While training the neural network models, a pair of noisy and ground truth datasets must be provided to the model.…”

Section: Introductionmentioning

confidence: 99%

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Artifact suppression for breast specimen imaging in micro CBCT using deep learning

Aootaphao,

Puttawibul,

Thajchayapong

et al. 2024

BMC Med Imaging

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Background Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen images. Furthermore, the presence of a metallic-needle marker on a breast specimen causes metal artifacts that are prominently visible in the images. In this work, we propose a deep learning-based approach for suppressing both streak and metal artifacts in CBCT. Methods In this work, sinogram datasets acquired from CBCT and a small number of projections containing metal objects were used. The sinogram was first modified by removing metal objects and up sampling in the angular direction. Then, the modified sinogram was initialized by linear interpolation and synthesized by a modified neural network model based on a U-Net structure. To obtain the reconstructed images, the synthesized sinogram was reconstructed using the traditional filtered backprojection (FBP) approach. The remaining residual artifacts on the images were further handled by another neural network model, ResU-Net. The corresponding denoised image was combined with the extracted metal objects in the same data positions to produce the final results. Results The image quality of the reconstructed images from the proposed method was improved better than the images from the conventional FBP, iterative reconstruction (IR), sinogram with linear interpolation, denoise with ResU-Net, sinogram with U-Net. The proposed method yielded 3.6 times higher contrast-to-noise ratio, 1.3 times higher peak signal-to-noise ratio, and 1.4 times higher structural similarity index (SSIM) than the traditional technique. Soft tissues around the marker on the images showed good improvement, and the mainly severe artifacts on the images were significantly reduced and regulated by the proposed. method. Conclusions Our proposed method performs well reducing streak and metal artifacts in the CBCT reconstructed images, thus improving the overall breast specimen images. This would be beneficial for clinical use.

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Section: Structure Of the Proposed Methods With Deep Learningmentioning

confidence: 99%

Section: Structure Of the Proposed Methods With Deep Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artifact suppression for breast specimen imaging in micro CBCT using deep learning

Aootaphao,

Puttawibul,

Thajchayapong

et al. 2024

BMC Med Imaging

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“…Traditional postprocessing methods can be divided into dictionary learning methods [22]- [25], NLM filtering methods [26], [27], and block-matching three-dimensional filtering (BM3D) methods [28], [29]. More recently, deep learning (DL) methods [30]- [51] have gradually become popular methods for LDCT. Because the noise in LDCT images may not obey a certain distribution, the traditional methods usually generate estimated results that contain noise and artifacts.…”

Section: Introductionmentioning

confidence: 99%

Deep Cascade Residual Networks (DCRNs): Optimizing an Encoder–Decoder Convolutional Neural Network for Low-Dose CT Imaging

Huang

Chen

Quan

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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To suppress noise and artifacts caused by the reduced radiation exposure in low-dose computed tomography, several deep learning (DL)-based image restoration methods have been proposed over the past few years. Many of these popular DL-based methods adopt an encoder-decoder framework, for instance, the residual encoder-decoder convolutional neural network. However, this popular framework may suffer from information loss for continual downsampling operations. In this article, deep cascaded residual networks (DCRNs) are proposed to optimize the popular encoder-decoder network. First, cross up-and downsampling operations as well as attention extraction are substitutes for the strict "downsampling and then upping" principle. What is more, four hybrid loss functions, namely, mean absolute error, edge loss, perceptual loss and adversarial loss, are engaged to achieve better visual effects and suppress noise. The experiments are conducted on three individual clinical CT datasets: dental CT data collected with a scanner manufactured by Zhongke Tianyue Company (ZTC), data from the American Association of Physicists in Medicine (AAPM) Challenge, and data collected with a commercial CT scanner from United Imaging Healthcare (UIH). The experimental results indicate the effective noise reduction and detail preservation capabilities of the proposed methods under different radiation dose-reduction strategies.

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“…A parameter-dependent framework (PDF)based RED-CNN network has also been introduced, which is trained simultaneously via two multilayer perceptrons (MLPs) that are used for modulating the feature maps of CT reconstruction process (Xia et al, 2021a). A ResNet merged with U-Net is able to learn both local and global image features, avoiding the vanishing gradient system, which is similar to the objective of DU-GAN but has a very comprehensive architecture while achieving the same results (Liu et al, 2021). The feasibility of a residual neural network was also explored by applying the concept of transfer learning for LDCT image denoising especially when an unknown noise level is present (Zhong et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Low Dose CT Denoising by ResNet With Fused Attention Modules and Integrated Loss Functions

Marcos¹,

Alirezaie²,

Babyn³

2022

Front. Signal Process.

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X-ray computed tomography (CT) is a non-invasive medical diagnostic tool that has raised public concerns due to the associated health risks of radiation dose to patients. Reducing the radiation dose leads to noise artifacts, making the low-dose CT images unreliable for diagnosis. Hence, low-dose CT (LDCT) image reconstruction techniques have offered a new research area. In this study, a deep neural network is proposed, specifically a residual network (ResNet) using dilated convolution, batch normalization, and rectified linear unit (ReLU) layers with fused spatial- and channel-attention modules to enhance the quality of LDCT images. The network is optimized using the integration of per-pixel loss, perceptual loss via VGG16-net, and dissimilarity index loss. Through an ablation experiment, these functions show that they could effectively prevent edge oversmoothing, improve image texture, and preserve the structural details. Finally, comparative experiments showed that the qualitative and quantitative results of the proposed network outperform state-of-the-art denoising models such as block-matching 3D filtering (BM3D), Markovian-based patch generative adversarial network (patch-GAN), and dilated residual network with edge detection (DRL-E-MP).

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Low-dose CT imaging via cascaded ResUnet with spectrum loss

Cited by 10 publications

References 33 publications

Artifact suppression for breast specimen imaging in micro CBCT using deep learning

Artifact suppression for breast specimen imaging in micro CBCT using deep learning

Deep Cascade Residual Networks (DCRNs): Optimizing an Encoder–Decoder Convolutional Neural Network for Low-Dose CT Imaging

Low Dose CT Denoising by ResNet With Fused Attention Modules and Integrated Loss Functions

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