ZHICHENG ZHANG received his Ph.D from University of Chinese Academy of Sciences and B.S. degree from Sun Yat-sen University. He is currently a postdoctoral fellow in Stanford University. From 2017 to 2018, he had been a Visiting scholar with the Virginia Tech-Wake Forest University, School of Biomedical Engineering and Sciences, Virginia Polytechnic Institute and State University, USA. His research interests are medical data analysis, computer vision and deep learning.

Professional Education

  • Ph.D, University of Chinese Academy of Sciences
  • B.S, Sun Yat-Sen University

Stanford Advisors

  • Lei Xing, Postdoctoral Faculty Sponsor


All Publications

  • Scatter correction for a clinical cone-beam CT system using an optimized stationary beam blocker in a single scan MEDICAL PHYSICS Liang, X., Jiang, Y., Zhao, W., Zhang, Z., Luo, C., Xiong, J., Yu, S., Yang, X., Sun, J., Zhou, Q., Niu, T., Xie, Y. 2019; 46 (7): 3165?79

    View details for DOI 10.1002/mp.13568

    View details for Web of Science ID 000475671900022

  • Scatter correction for a clinical cone-beam CT system using an optimized stationary beam blocker in a single scan. Medical physics Liang, X., Jiang, Y., Zhao, W., Zhang, Z., Luo, C., Xiong, J., Yu, S., Yang, X., Sun, J., Zhou, Q., Niu, T., Xie, Y. 2019


    PURPOSE: Scatter contamination in the cone-beam CT (CBCT) leads to CT number inaccuracy, spatial non-uniformity, and loss of image contrast. In our previous work, we proposed a single scan scatter correction approach using a stationary partial beam blocker. Although the previous method works effectively on a tabletop CBCT system, it fails to achieve high image quality on a clinical CBCT system mainly due to the wobble of the LINAC gantry during scan acquisition. Due to the mechanical deformation of CBCT gantry, the wobbling effect is observed in the clinical CBCT scan, and more missing data present using the previous blocker with the uniformly distributed lead strips.METHODS: An optimal blocker distribution is proposed to minimize the missing data. In the objective function of the missing data, the motion of the beam blocker in each projection is estimated using the segmentation due to its high contrast in the blocked area. The scatter signals from the blocker are also estimated using an air scan with the inserted blocker. The final image is generated using the forward projection to compensate for the missing data.RESULTS: On the CatphanŠ504 phantom, our approach reduces the average CT number error from 86 Hounsfield unit (HU) to 9 HU and improves the image contrast by a factor of 1.45 in the high-contrast rods. On a head patient, the CT number error is reduced from 97 HU to 6 HU in the soft-tissue region and the image spatial non-uniformity is decreased from 27% to 5%.CONCLUSIONS: The results suggest that the proposed method is promising for clinical applications. This article is protected by copyright. All rights reserved.

    View details for PubMedID 31055835

  • A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE Zou, L., Yu, S., Meng, T., Zhang, Z., Liang, X., Xie, Y. 2019: 6509357


    This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

    View details for DOI 10.1155/2019/6509357

    View details for Web of Science ID 000464727600001

    View details for PubMedID 31019547

    View details for PubMedCentralID PMC6452645

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