A Virtual Monochromatic Imaging Method for Spectral CT Based on Wasserstein Generative Adversarial Network With a Hybrid Loss

Shi, Zaifeng; Li, Jinzhuo; Li, Huilong; Hu, Qixing; Cao, Qingjie

doi:10.1109/access.2019.2934508

Cited by 12 publications

(9 citation statements)

References 48 publications

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“…In addition, employing two generators 33 or discriminators 34 in the adversarial training process have been proven effective in solving the mode collapse problem. Furthermore, the loss function of the generator is essential in network learning, and different loss functions or their combinations have been used to accomplish specific learning tasks efficiently 35 . For example, Yang et al coupled the mean squared error (MSE) loss of both image and frequency domains as well as a perceptual loss to provide superior reconstruction images for magnetic resonance imaging 36 …”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the loss function of the generator is essential in network learning, and different loss functions or their combinations have been used to accomplish specific learning tasks efficiently. 35 For example, Yang et al coupled the mean squared error (MSE) loss of both image and frequency domains as well as a perceptual loss to provide superior reconstruction images for magnetic resonance imaging. 36 Inspired by the promising performance of adversarial training, an image-based material decomposition method using dual interactive Wasserstein generative adversarial networks (DIWGAN) was proposed to solve the problem of noise magnification and to remove beam-hardening artifacts from DECT material decomposition.…”

Section: Introductionmentioning

confidence: 99%

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

Shi

Cao

et al. 2021

Medical Physics

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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...

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Section: Introductionmentioning

confidence: 99%

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

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“… 26 Also, a Wasserstein generative adversarial network with a hybrid loss transforms several polyenergetic images in different energy bins to VM images. 27 Furthermore, using a convolutional neural network, DECT imaging was achieved from standard single-spectrum CT data. 28 , 29 , 30 Recently, a deep generative model was developed to generate energy-resolved CT images in multiple energy bins from given energy-integrating CT images using a generative adversarial network framework.…”

Section: Introductionmentioning

confidence: 99%

Virtual Monoenergetic CT Imaging via Deep Learning

Cong

Xi²,

Fitzgerald

et al. 2020

Patterns

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Summary Conventional single-spectrum computed tomography (CT) reconstructs a spectrally integrated attenuation image and reveals tissues morphology without any information about the elemental composition of the tissues. Dual-energy CT (DECT) acquires two spectrally distinct datasets and reconstructs energy-selective (virtual monoenergetic [VM]) and material-selective (material decomposition) images. However, DECT increases system complexity and radiation dose compared with single-spectrum CT. In this paper, a deep learning approach is presented to produce VM images from single-spectrum CT images. Specifically, a modified residual neural network (ResNet) model is developed to map single-spectrum CT images to VM images at pre-specified energy levels. This network is trained on clinical DECT data and shows excellent convergence behavior and image accuracy compared with VM images produced by DECT. The trained model produces high-quality approximations of VM images with a relative error of less than 2%. This method enables multi-material decomposition into three tissue classes, with accuracy comparable with DECT.

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“…This includes a growing body of work on CNN‐based postreconstruction denoising for reproducing full‐dose CT data from low‐dose CT data 9–11 . More relevant here, this also includes several papers demonstrating the advantages of spatial contextual information in the problems of material decomposition 12–14 and the synthesis of noncontrast images 15 and monochromatic images 16 . More broadly, application of deep learning to the problem of spectral extrapolation is motivated by prior successes in a number of closely related image processing problems, including inpainting, 17 pan‐sharpening, 18 and assigning color to grayscale images 19 …”

Section: Introductionmentioning

confidence: 99%

Deep learning based spectral extrapolation for dual‐source, dual‐energy x‐ray computed tomography

et al. 2020

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Purpose: Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is accomplished by estimating missing projection data based on the imaging chain with the full FoV and then by appropriately truncating the analytical reconstruction of the data with the smaller FoV. While this approach works well in many clinical applications, there are applications which would benefit from spectral contrast estimates over the larger FoV (spectral extrapolation)-e.g. model-based iterative reconstruction, contrast-enhanced abdominal imaging of large patients, interior tomography, and combined temporal and spectral imaging. Methods: To document the fidelity of spectral extrapolation and to prototype a deep learning algorithm to perform it, we assembled a data set of 50 dual-source, dual-energy abdominal x-ray CT scans (acquired at Duke University Medical Center with 5 Siemens Flash scanners; chain A: 50 cm FoV, 100 kV; chain B: 33 cm FoV, 140 kV + Sn; helical pitch: 0.8). Data sets were reconstructed using ReconCT (v14.1, Siemens Healthineers): 768 × 768 pixels per slice, 50 cm FoV, 0.75 mm slice thickness, "Dual-Energy-WFBP" reconstruction mode with dual-source data completion. A hybrid architecture consisting of a learned piecewise linear transfer function (PLTF) and a convolutional neural network (CNN) was trained using 40 scans (five scans reserved for validation, five for testing). The PLTF learned to map chain A spectral contrast to chain B spectral contrast voxel-wise, performing an image domain analog of dual-source data completion with approximate spectral reweighting. The CNN with its U-net structure then learned to improve the accuracy of chain B contrast estimates by copying chain A structural information, by encoding prior chain A, chain B contrast relationships, and by generalizing feature-contrast associations. Training was supervised, using data from within the 33-cm chain B FoV to optimize and assess network performance. Results: Extrapolation performance on the testing data confirmed our network's robustness and ability to generalize to unseen data from different patients, yielding maximum extrapolation errors of 26 HU following the PLTF and 7.5 HU following the CNN (averaged per target organ). Degradation of network performance when applied to a geometrically simple phantom confirmed our method's reliance on feature-contrast relationships in correctly inferring spectral contrast. Integrating our image domain spectral extrapolation network into a standard dual-source, dual-energy processing pipeline for Siemens Flash scanner data yielded spectral CT data with adequate fidelity for the generation of both 50 keV monochromatic images and material decomposition images over a 30-cm FoV for chain B when only 20 cm of chain B data were available for spectral extrapolation. Conclusions: Even with a moderate amount of training data, deep learning ...

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A Virtual Monochromatic Imaging Method for Spectral CT Based on Wasserstein Generative Adversarial Network With a Hybrid Loss

Cited by 12 publications

References 48 publications

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

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

Virtual Monoenergetic CT Imaging via Deep Learning

Deep learning based spectral extrapolation for dual‐source, dual‐energy x‐ray computed tomography

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