Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network

Xu, Yifu; Yan, Bin; Zhang, Jingfang; Chen, Jian; Zeng, Lei; Wang, Linyuang

doi:10.1155/2018/2527516

Cited by 30 publications

(20 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Noise amplification during material decomposition and spectral post-processing limits sensitivity to contrast materials and low-contrast features, particularly for low-dose acquisition protocols. Supervised training of CNNs represents an ideal solution to this problem because networks can learn to identify valid combinations of spatial features and spectral contrast, enforcing data-driven priors in a way that is difficult to reproduce with analytical approaches [104,109,110]. Similarly, supervised training has been applied to correct or compensate for sources of spectral distortion inherent in photon-counting CT [111,112].…”

Section: Spectral Processingmentioning

confidence: 99%

Advances in micro-CT imaging of small animals

Clark

Badea

2021

Physica Medica

View full text Add to dashboard Cite

Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research. Methods: Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-theart hardware and ongoing challenges and promising research directions in the field. Results: Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT. Conclusions: All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.

show abstract

Section: Spectral Processingmentioning

confidence: 99%

Advances in micro-CT imaging of small animals

Clark

Badea

2021

Physica Medica

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning (DL) technology has been extensively used in CT imaging due to its non-linear feature extraction and modeling capabilities (23)(24)(25)(26)(27). In the field of material decomposition, researchers have tried to apply DL to DECT to solve the problem of noise amplification in material decomposition (28,29). Clark et al used the existing U-Net (30) architecture to obtain good material decomposition performance (31).…”

Section: Introductionmentioning

confidence: 99%

One half-scan dual-energy CT imaging using the Dual-domain Dual-way Estimated Network (DoDa-Net) model

Wang¹,

Cai²,

Liang³

et al. 2022

Quant Imaging Med Surg

Self Cite

View full text Add to dashboard Cite

Background: Compared with single-energy computed tomography (CT), dual-energy CT (DECT) can distinguish materials better. However, most DECT reconstruction theories require two full-scan projection datasets of different energies, and this requirement is hard to meet, especially for cases where a physical blockage disables a full circular rotation. Thus, it is critical to relax the requirements of data acquisition to promote the application of DECT.Methods: A flexible one half-scan DECT scheme is proposed, which acquires two projection datasets on two-quarter arcs (one for each energy). The limited-angle problem of the one half-scan DECT scheme can be solved by a reconstruction method. Thus, a dual-domain dual-way estimation network called DoDa-Net is proposed by utilizing the ability of deep learning in non-linear mapping. Specifically, the dual-way mapping Generative Adversarial Network (DM-GAN) was first designed to mine the relationship between two different energy projection data. Two half-scan projection datasets were obtained, the data of which was twice that of the original projection dataset. Furthermore, the data transformation from the projection domain to the image domain was realized by the total variation (TV)-based method. In addition, the image processing network (Im-Net) was employed to optimize the image domain data. Results:The proposed method was applied to a digital phantom and real anthropomorphic head phantom data to verify its effectiveness. The reconstruction results of the real data are encouraging and prove the proposed method's ability to suppress noise while preserving image details. Also, the experiments conducted on simulated data show that the proposed method obtains the closest results to the ground truth among the comparison methods. For low-and high-energy reconstruction, the peak signal-to-noise ratio (PSNR) of the proposed method is as high as 40.3899 dB and 40.5573 dB, while the PSNR of other methods is lower than 36.5200 dB. Compared with FBP, TV, and other GAN-based methods, the proposed method reduces root mean square error (RMSE) by, respectively, 0.0124, 0.0037, and 0.0016 for low-energy reconstruction, and 0.0102, 0.0028, and 0.0015 for high-energy reconstruction. Conclusions:The developed DoDa-Net model for the proposed one half-scan DECT scheme consists of two stages. In stage one, DM-GAN is used to realize the dual map of projection data. In stage two, the TV-based method is employed to transform the data from the projection domain to the image domain. Furthermore, the reconstructed image is processed by the Im-Net. According to the experimental results of qualitative and quantitative evaluation, the proposed method has advantages in detail preservation, indicating the potential of the proposed method in one half-scan DECT reconstruction.

show abstract

“…Badea et al proposed to use convolutional neural networks (CNNs) for spectral micro‐CT material decomposition and achieved satisfactory results 24 . Additionally, Xu et al adopted a fully convolutional network (FCN) to decompose DECT images 25 . To establish a tight connection between a decomposition model and a network, Zhang et al 26 designed a butterfly network (Butterfly‐Net) to fully optimize the performances of networks and achieved promising material decomposition.…”

Section: Introductionmentioning

confidence: 99%

A material decomposition method for dual‐energy CT via dual interactive Wasserstein generative adversarial networks

Shi

Cao

et al. 2021

Medical Physics

View full text Add to dashboard Cite

Purpose Dual‐energy computed tomography (DECT) is highly promising for material characterization and identification, whereas reconstructed material‐specific images are affected by magnified noise and beam‐hardening artifacts. Although various DECT material decomposition methods have been proposed to solve this problem, the quality of the decomposed images is still unsatisfactory, particularly in the image edges. In this study, a data‐driven approach using dual interactive Wasserstein generative adversarial networks (DIWGAN) is developed to improve DECT decomposition accuracy and perform edge‐preserving images. Methods In proposed DIWGAN, two interactive generators are used to synthesize decomposed images of two basis materials by modeling the spatial and spectral correlations from input DECT reconstructed images, and the corresponding discriminators are employed to distinguish the difference between the generated images and labels. The DECT images reconstructed from high‐ and low‐energy bins are sent to two generators separately, and each generator synthesizes one material‐specific image, thereby ensuring the specificity of the network modeling. In addition, the information from different energy bins is exploited through the feature sharing of two generators. During decomposition model training, a hybrid loss function including L1 loss, edge loss, and adversarial loss is incorporated to preserve the texture and edges in the generated images. Additionally, a selector is employed to define the generator that should be trained in each iteration, which can ensure the modeling ability of two different generators and improve the material decomposition accuracy. The performance of the proposed method is evaluated using digital phantom, XCAT phantom, and real data from a mouse. Results On the digital phantom, the regions of bone and soft tissue are strictly and accurately separated using the trained decomposition model. The material densities in different bone and soft‐tissue regions are near the ground truth, and the error of material densities is lower than 3 mg/ml. The results from XCAT phantom show that the material‐specific images generated by directed matrix inversion and iterative decomposition methods have severe noise and artifacts. Regarding to the learning‐based methods, the decomposed images of fully convolutional network (FCN) and butterfly network (Butterfly‐Net) still contain varying degrees of artifacts, while proposed DIWGAN can yield high quality images. Compared to Butterfly‐Net, the root‐mean‐square error (RMSE) of soft‐tissue images generated by the DIWGAN decreased by 0.01 g/ml, whereas the peak‐signal‐to‐noise ratio (PSNR) and structural similarity (SSIM) of the soft‐tissue images reached 31.43 dB and 0.9987, respectively. The mass densities of the decomposed materials are nearest to the ground truth when using the DIWGAN method. The noise standard deviation of the decomposition images reduced by 69%, 60%, 33%, and 21% compared with direct matrix inversion, iterative decomposition, FCN, and Butterf...

show abstract

Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network

Cited by 30 publications

References 24 publications

Advances in micro-CT imaging of small animals

Advances in micro-CT imaging of small animals

One half-scan dual-energy CT imaging using the Dual-domain Dual-way Estimated Network (DoDa-Net) model

A material decomposition method for dual‐energy CT via dual interactive Wasserstein generative adversarial networks

Contact Info

Product

Resources

About