Low-dose CT reconstruction method based on prior information of normal-dose image

2020 ◽  
Vol 28 (6) ◽  
pp. 1091-1111
Author(s):  
Zixiang Chen ◽  
Qiyang Zhang ◽  
Chao Zhou ◽  
Mengxi Zhang ◽  
Yongfeng Yang ◽  
...  
Keyword(s):  
Prior Information ◽  
Low Dose ◽  
Background Radiation ◽  
Ct Scanning ◽  
Superior Performance ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Normal Dose ◽  
Low Dose Ct

BACKGROUND: Radiation risk from computed tomography (CT) is always an issue for patients, especially those in clinical conditions in which repeated CT scanning is required. For patients undergoing repeated CT scanning, a low-dose protocol, such as sparse scanning, is often used, and consequently, an advanced reconstruction algorithm is also needed. OBJECTIVE: To develop a novel algorithm used for sparse-view CT reconstruction associated with the prior image. METHODS: A low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) involving a transformed model for attenuation coefficients of the object to be reconstructed and prior information application in the forward-projection process was used to reconstruct CT images from sparse-view projection data. A digital extended cardiac-torso (XCAT) ventral phantom and a diagnostic head phantom were employed to evaluate the performance of the proposed PI-NDI method. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR) and mean percent absolute error (MPAE) of the reconstructed images were measured for quantitative evaluation of the proposed PI-NDI method. RESULTS: The reconstructed images with sparse-view projection data via the proposed PI-NDI method have higher quality by visual inspection than that via the compared methods. In terms of quantitative evaluations, the RMSE measured on the images reconstructed by the PI-NDI method with sparse projection data is comparable to that by MLEM-TV, PWLS-TV and PWLS-PICCS with fully sampled projection data. When the projection data are very sparse, images reconstructed by the PI-NDI method have higher PSNR values and lower MPAE values than those from the compared algorithms. CONCLUSIONS: This study presents a new low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) for sparse-view CT image reconstruction. The experimental results validate that the new method has superior performance over other state-of-art methods.

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging

2021 ◽  
Vol 29 (1) ◽  
pp. 91-109
Author(s):  
Zhiwei Feng ◽  
Ailong Cai ◽  
Yizhong Wang ◽  
Lei Li ◽  
Li Tong ◽  
...  
Keyword(s):  
Neural Network ◽  
Low Dose ◽  
Structural Information ◽  
Superior Performance ◽  
Projection Data ◽  
Image Domain ◽  
Low Dose Ct ◽  
Quantitative Metrics ◽  
Complementary Role ◽  
Ct Technology

The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a low radiation dose in CT can result in severe artifacts and noise in the captured images, thus affecting the diagnosis. Therefore, in this study, we investigate a dual residual convolution neural network (DRCNN) for low-dose CT (LDCT) imaging, whereby the CT images are reconstructed directly from the sinogram by integrating analytical domain transformations, thus reducing the loss of projection information. With this new framework, feature extraction is performed simultaneously on both the sinogram-domain sub-net and the image-domain sub-net, which utilize the residual shortcut networks and play a complementary role in suppressing the projection noise and reducing image error. This new DRCNN approach helps not only decrease the sinogram noise but also preserve significant structural information. The experimental results of simulated and real projection data demonstrate that our DRCNN achieve superior performance over other state-of-art methods in terms of visual inspection and quantitative metrics. For example, comparing with RED-CNN and DP-ResNet, the value of PSNR using our DRCNN is improved by nearly 3 dB and 1 dB, respectively.

Low-dose CT reconstruction with MRF prior predicted from patch samples of normal-dose CT database

2018 ◽  
Author(s):  
Junyan Rong ◽  
Yuanke Zhang ◽  
Yuxiang Xing ◽  
Peng Gao ◽  
Tianshuai Liu ◽  
...  
Keyword(s):  
Low Dose ◽  
Ct Reconstruction ◽  
Normal Dose ◽  
Low Dose Ct

Median prior constrained TV algorithm for sparse view low-dose CT reconstruction

2015 ◽  
Vol 60 ◽  
pp. 117-131 ◽  
Author(s):  
Yi Liu ◽  
Hong Shangguan ◽  
Quan Zhang ◽  
Hongqing Zhu ◽  
Huazhong Shu ◽  
...  
Keyword(s):  
Low Dose ◽  
Ct Reconstruction ◽  
Low Dose Ct

scholarly journals Low-dose CT reconstruction using spatially encoded nonlocal penalty

2017 ◽  
Vol 44 (10) ◽  
pp. e376-e390 ◽  
Author(s):  
Kyungsang Kim ◽  
Georges El Fakhri ◽  
Quanzheng Li
Keyword(s):  
Low Dose ◽  
Ct Reconstruction ◽  
Low Dose Ct

Weighted adaptive non-local dictionary for low-dose CT reconstruction

2021 ◽  
Vol 180 ◽  
pp. 107871
Author(s):  
Haijun Yu ◽  
Shaoyu Wang ◽  
Weiwen Wu ◽  
Changcheng Gong ◽  
Linbo Wang ◽  
...  
Keyword(s):  
Low Dose ◽  
Ct Reconstruction ◽  
Low Dose Ct ◽  
Non Local

scholarly journals Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Chao Tang ◽  
Jie Li ◽  
Linyuan Wang ◽  
Ziheng Li ◽  
Lingyun Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Radiation Dose ◽  
Prior Information ◽  
Low Dose ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Widespread Application ◽  
Image Information ◽  
Low Dose Ct ◽  
One To One

The widespread application of X-ray computed tomography (CT) in clinical diagnosis has led to increasing public concern regarding excessive radiation dose administered to patients. However, reducing the radiation dose will inevitably cause server noise and affect radiologists’ judgment and confidence. Hence, progressive low-dose CT (LDCT) image reconstruction methods must be developed to improve image quality. Over the past two years, deep learning-based approaches have shown impressive performance in noise reduction for LDCT images. Most existing deep learning-based approaches usually require the paired training dataset which the LDCT images correspond to the normal-dose CT (NDCT) images one-to-one, but the acquisition of well-paired datasets requires multiple scans, resulting the increase of radiation dose. Therefore, well-paired datasets are not readily available. To resolve this problem, this paper proposes an unpaired LDCT image denoising network based on cycle generative adversarial networks (CycleGAN) with prior image information which does not require a one-to-one training dataset. In this method, cyclic loss, an important trick in unpaired image-to-image translation, promises to map the distribution from LDCT to NDCT by using unpaired training data. Furthermore, to guarantee the accurate correspondence of the image content between the output and NDCT, the prior information obtained from the result preprocessed using the LDCT image is integrated into the network to supervise the generation of content. Given the map of distribution through the cyclic loss and the supervision of content through the prior image loss, our proposed method can not only reduce the image noise but also retain critical information. Real-data experiments were carried out to test the performance of the proposed method. The peak signal-to-noise ratio (PSNR) improves by more than 3 dB, and the structural similarity (SSIM) increases when compared with the original CycleGAN without prior information. The real LDCT data experiment demonstrates the superiority of the proposed method according to both visual inspection and quantitative evaluation.

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