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您的检索式：作者名="Zhidu Zhang"
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| 1 | Improved projection-based energy weighting for spectral CT显示文摘Purpose Conventional X-ray CT scanners have limited ability to distinguish low-contrast substances.However,spectral CTs with photon counting detectors can identify photon energy and utilize spectral information,which is expected to achieve improved contrast.Energy weighting is a kind of reconstruction method for spectral CT.By assigning appropriate weight for each energy channel,the image contrast can be improved.Hence,how to determine the optimal weights is very important.Methods In this paper,we developed an improved projection-based energy weighting model for spectral CT.In this model,the object thickness of low-density materials is assumed as a constant,and the measured spectrum distribution is used to calculate the weight coefficients.Both phantom and tissue experiments were conducted in spectral CT scanner.Results The results showed that the thickness of low-density materials has little influence on the energy weight,so it can be regarded as a constant.For low-contrast phantom,the contrast-to-noise ratio was improved~32%by the proposed projectionbased weighting method.Conclusions The improved projection-based energy weighting model is effective in practice.It can increase the contrast of low-density materials. | Zhiwei Cheng Mohan Li Qiong Xu Zhidu Zhang Jinming Hu Cunfeng Wei Long Wei Zhe Wang | 2019 | Radiation Detection Technology and Methods2019，3，3： | 0 |
| 2 | A study on noise reduction for dual-energy CT material decomposition with autoencoder显示文摘Purpose A major challenge for the material decomposition task of the dual-energy computed tomography(DECT)is the algorithm often suffers from heavy noise in the results.The purpose of this study is to propose a scheme to increase the noise performance of material decomposition.Methods The scheme we propose in this paper is to apply an autoencoder-based denoising procedure to the photon-counting DECT images before they are fed into the material decomposition algorithm.We implement the autoencoder(AE)by stacking a series of convolutional and deconvolutional layers.The decomposition technique adopted in our work is an iterative method using least squares estimation with the Huber loss function.The noises of the input and the output of material decomposition are analyzed with both simulated data and real data.Phantom and chicken wing experiments are conducted with a photoncounting-based spectral CT scanner to evaluate the proposed material decomposition scheme.Results The noise analysis of the input and the output of material decomposition demonstrates a positive correlation between them.Comparative experiment indicates a noise reduction in the output density maps for 26.07%to 35.65%after the autoencoder pre-processing is applied.The resultant contrast-to-noise ratio is largely increased,correspondingly.Conclusions By utilizing the additional autoencoder denoising step,the material decomposition algorithm achieves an improvement in the noise performance of the resultant density maps. | Mohan Li Zhe Wang Qiong Xu Zhidu Zhang Zhiwei Cheng Shuangquan Liu Baodong Liu Cunfeng Wei Long Wei | 2019 | Radiation Detection Technology and Methods2019，3，3： | 0 |
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