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黄静

作者:  来源:   阅读量:  发布时间:2017-11-10 10:32:24

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姓名:黄静

职称:副教授

联系邮箱:hjing@smu.edu.cn


v 学习经历

2003.09-2006.07 武汉大学,计算机应用技术,博士

2000.09-2003.07 武汉大学,计算数学,硕士

1996.09-2000.07 武汉大学,计算数学,学士


v 工作经历

2012.12至今 南方医科大学生物医学工程学院数学物理系,副教授

2007.01-2012.11 南方医科大学生物医学工程学院数学物理系,讲师

2006.07-2006.12 南方医科大学生物医学工程学院数学物理系,助教


v 研究方向

脑卒中灌注CT精准成像新技术研究





v 主要学术任职


v 主要获奖情况(科研获奖、人才类奖项)

1. 广东省技术发明奖一等奖,高端宽体CT低剂量成像关键技术与系统研发及应用,第八完成人,2019年.


v 主要科研课题

1. 国家自然科学基金,61571214,基于交错投影采样的低剂量脑卒中CT成像方法研究,2016.01-2019.12,79.98万元,主持

2. 广东省自然科学基金,2015A030313271,多种先验信息导引的低剂量Brain-CTP优质成像新方法研究,2015.08-2018.08,10万元,主持

3. 广州市科技计划项目,201804010448,新型PET/荧光多模态分子探针在前列腺癌精准医疗中的应用,2018.04-2021.03,10万元,参与

4. 广州市科技计划项目,201705030009,广州市医用放射成像与检测技术重点实验室,2017.01-2019.12,200万元,参与

5. 广东省应用型科技研发专项,2015B020233008,数字乳腺层析成像系统关键技术研发与整机研制,2016.01-2019.12,500万元,参与


v 代表性论文

[1] L. Yao, D. Zeng, G. Chen, Y. Liao, S. Li, Y. Zhang, Y. Wang, X. Tao, S. Niu, Q. Lv, Z. Bian, J. Ma*, J. Huang*, Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model, Physics in Medicine and Biology, vol.64, pp. 0350183, 2019.

[2] X. Jia, Y. Liao, D. Zeng, Ha. Zhang, Y. Zhang, J. He, Z. Bian, Y. Wang, X. Tao, Z. Liang, J. Huang*, J. Ma*, Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image, Physics in medicine and biology, vol. 63, pp. 225020, 2018.

[3] S. Li, D.g Zeng, J. Peng, Z. Bian, H. Zhang, Q. Xie, Y. Wang, Y. Liao, S. Zhang, J. Huang, D. Meng, Z. Xu, and J. Ma*, An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition with Spatial-Temporal Total Variation Regularization, IEEE Transactions on Medical Imaging, vol. 38, no. 2, pp. 360-370, 2018.

[4] C. Gu, D. Zeng, J. Lin, S. Li, J. He, H. Zhang, Z. Bian, S. Niu, Z. Zhang, J. Huang, B. Chen, D. Zhao, W. Chen, J. Ma*, Promote quantitative ischemia imaging via myocardial perfusion CT iterative reconstruction with tensor total generalized variation regularization, Physics in Medicine and Biology, vol. 63, no. 12, pp. 125009, 2018.

[5] J. Lin, H. Zhang, J. Huang, Z. Bian, S. Zhang, Y. Wang, Y. Liao, S. Li, H. Zhang, D. Zeng*, J. Ma*, Iterative reconstruction for low dose dual energy CT using information-divergence constrained spectral redundancy information, Journal of X-ray science and technology, vol. 26, no. 2, pp. 311-330, 2018.

[6] D. Zeng, Q. Xie, W. Cao, J. Lin, H. Zhang, S. Zhang, J. Huang, Z. Bian, D. Meng, Z. Xu, Z. Liang, W. Chen, and J. Ma*, Low-dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-basis-representation Tensor Sparsity Regularization, IEEE Transactions on Medical Imaging, vol. 36, pp. 2546-2556, 2017.

[7] D. Zeng, X. Zhang, Z. Bian, J. Huang, H. Zhang, L. Lu, W. Lyu, J. Zhang, Qi. Feng, W. Chen, and J. Ma*, Cerebral Perfusion Computed Tomography Deconvolution via Structure Tensor Total Variation Regularization, Medical Physics, vol. 43, no. 5, pp. 2091-2107, 2016.

[8] J. Li, S. Niu, J. Huang*, Z. Bian, Q. Feng, G. Yu, Z. Liang, W. Chen, and J. Ma. An efficient Augmented Lagrangian method for statistical X-ray CT image reconstruction, PLOS ONE, vol. 10, no.10, pp. e0140579, 2015.

[9] D. Zeng, J. Huang, Z. Bian, S. Niu, H. Zhang, Q. Feng, Z. Liang, and J. Ma*, A simple low-dose X-ray CT simulation from high-dose scan, IEEE Transactions on Nuclear Science, vol. 62, pp. 2226-2233, 2015, 2015.

[10] J. Huang, Y. Zhang, J. Ma*, H. Zhang, Z. Bian, D. Zeng, Q. Feng, Z. Liang, and W. Chen.  Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior, Plos One, vol.8, no. 11, pp. e79709, 2013.



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