Low-dose CT statistical iterative reconstruction via modified MRF regularization

Shangguan, Hong; Zhang, Quan; Liu, Yi; Cui, Xueying; Bai, Yunjiao; Gui, Zhiguo

doi:10.1016/j.cmpb.2015.10.004

Cited by 30 publications

(11 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Iterative reconstruction can solve the ill‐posed inverse problem for downsampled datasets by iteratively estimating the underlying denoized image. Various priors such as nonlocal means (NLM) priors, Markov random field (MRF) priors, total variation (TV)‐based priors and dictionary learning (DL) priors are used in the cost function. Although iterative reconstruction categories sometimes generate excellent outcomes, the reconstruction results still suffer from some detail loss and from remaining artifacts.…”

Section: Introductionmentioning

confidence: 99%

Artifact correction in low‐dose dental CT imaging using Wasserstein generative adversarial networks

Jiang

Sun

et al. 2019

Medical Physics

View full text Add to dashboard Cite

Purpose: In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low-dose x-ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning. Method: We used clinical dental CT data with low-dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high-quality labeled normal-dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m-WGAN, to remove artifacts and obtain high-quality CT dental images in a clinical dental CT examination environment. Results: The experimental results confirmed that the proposed algorithm effectively removes lowdose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low-dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m-WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m-WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning. Conclusions: The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone-beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low-dose dental CT artifact correction.

show abstract

Section: Introductionmentioning

confidence: 99%

Artifact correction in low‐dose dental CT imaging using Wasserstein generative adversarial networks

Jiang

Sun

et al. 2019

Medical Physics

View full text Add to dashboard Cite

show abstract

“…First, the regular term is designed, the objective function is optimized, and the iterative process stops until a better signal-to-noise ratio (SNR) is obtained. IR based methods, such as total variation (TV) (8), non-local means (NLM) (9), and Markov random field (MRF) prior (10), dictionary learning (11), and other techniques (12,13). Although the CT image reconstructed using the IR method is of high quality, it relies on the supplier's projection data.…”

Section: Introductionmentioning

confidence: 99%

Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network

Li¹,

Li²,

Li³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background: Computed tomography (CT) is widely used in medical diagnoses due to its ability to noninvasively detect the internal structures of the human body. However, CT scans with normal radiation doses can cause irreversible damage to patients. The radiation exposure is reduced with low-dose CT (LDCT), although considerable speckle noise and streak artifacts in CT images and even structural deformation may result, significantly undermining its diagnostic capability.Methods: This paper proposes a multistage network framework which gradually divides the entire process into 2-staged sub-networks to complete the task of image reconstruction. Specifically, a dilated residual convolutional neural network (DRCNN) was used to denoise the LDCT image. Then, the learned context information was combined with the channel attention subnet, which retains local information, to preserve the structural details and features of the image and textural information. To obtain recognizable characteristic details, we introduced a novel self-calibration module (SCM) between the 2 stages to reweight the local features, which realizes the complementation of information at different stages while refining feature information. In addition, we also designed an autoencoder neural network, using a self-supervised learning scheme to train a perceptual loss neural network specifically for CT images.Results: We evaluated the diagnostic quality of the results and performed ablation experiments on the loss function and network structure modules to verify each module's effectiveness in the network. Our proposed network architecture obtained high peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual information fidelity (VIF) values in terms of quantitative evaluation. In the analysis of qualitative results, our network structure maintained a better balance between eliminating image noise and preserving image details. Experimental results showed that our proposed network structure obtained better metrics and visual evaluation.Conclusions: This study proposed a new LDCT image reconstruction method by combining autoencoder perceptual loss networks with multistage convolutional neural networks (MSCNN). Experimental results showed that the newly proposed method has performance than other methods.

show abstract

“…diagnoses [2]. Extensive efforts such as optimal scan protocols [3], advanced hardware techniques [4], and advanced image reconstruction algorithms [5]- [8], have been made to improve the quality of reconstructed images from LDCT.…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising

et al. 2020

Self Cite

View full text Add to dashboard Cite

Low-dose computed tomography (LDCT) imaging has attracted tremendous attention because it reduces the potential cancer risk for patients by decreasing the radiation dose. However, reducing the radiation dose may cause image quality degradation due to the introduction of noise and artifacts. The details of pathological information mainly exist in the high-frequency domain of LDCT image. Therefore, some useful details may be lost or destroyed while removing the noise and artifacts. To address this problem, we propose a high-frequency sensitive generative adversarial network (HFSGAN). The new generator includes two sub-networks. One is the high-frequency domain U-Net, which is specially designed to deal with the high-frequency components decomposed from LDCT image. The other is image space U-Net, which is used to process information from the whole image of LDCT. In addition, the discriminator in HFSGAN adopts an inception module to increase the receptive field and width of network, and to extract the multi-scale features of the true and false images. The experiments show that the proposed network preserves more texture details of denoised image while removing noise and artifacts. Compared with the state-of-the-art networks, the proposed denoising method achieves better performance both quantitatively and visually. INDEX TERMS Low-dose CT, image denoising, GAN, U-Net, inception module.

show abstract

Low-dose CT statistical iterative reconstruction via modified MRF regularization

Cited by 30 publications

References 36 publications

Artifact correction in low‐dose dental CT imaging using Wasserstein generative adversarial networks

Artifact correction in low‐dose dental CT imaging using Wasserstein generative adversarial networks

Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network

High-Frequency Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising

Contact Info

Product

Resources

About