Xiaopeng Zong
Assistant Professor
Research Area: Medical Imaging
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Office: Room 322, BME Building
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Brief CV
Research Interests
Courses
Services to External Academic Communities
Publications
Lab Introduction

个人简历:

Dr. Xiaopeng Zong is a tenure-track Assistant Professor and Principal Investigator in the School of Biomedical Engineering, Director in the Brain MRI Physics Lab.  Dr. Zong obtained his Ph.D in condensed matter physics from Iowa State University.  After graduation, he has focused his research on illuminating MRI contrast mechanisms, developing new imaging methods, and applying MRI to clinical research.  Before joining ShanghaiTech, he was a Research Associate Professor in the Department of Radiology at University of North Carolina at Chapel Hill and served as the Faculty Imaging Advisor in the Biomedical Research Imaging Center at UNC. 

Dr. Zong has published 36 peer-reviewed journal articles in top international journals, one of which was selected as Editor’s Recommended Paper by Physical Review B.  He has presented his research findings in international conferences numerous times.  He has served as the Principal Investigator or played a major role in numerous federal and state government funded research projects in the United States.






研究领域:

  • MRI motion correction

  • Segmentation and quantification of small blood vessels

  • High-field high spatiotemporal resolution MRI

  • Cerebral small vessel disease

  • Perivascular spaces and the glymphatic system








教学与课程:



学术任职:

Ad hoc Reviewer for PLOS ONE,J. Cereb. Blood Flow Metab., Dove Press, IEEE Trans. Med. Imaging, Quant Imaging Med Surg, IEEE Access, and Frontiers in Neuroscience.







代表性论文:

MRI contrast mechanisms

  • X. Zong, P. Wang, Seong-Gi Kim, and T. Jin,Sensitivity and Signal Source of Amine Proton EXchange (APEX) and Amide Proton Transfer (APT) MRI in Cerebral Ischemia, Magn. Reson. Med. 71, 118 (2014).

  • X. Zong, Tae Kim, and Seong-Gi Kim, Contributions of dynamic venous blood volume versus oxygenation level changes to BOLD fMRI, NeuroImage 60, 2238 (2012).

  • X. Zong and J. Huang, Linear coupling of undershoot with BOLD response in ER-fMRI and nonlinear BOLD response in rapid-presentation ER-fMRI, NeuroImage 57, 391 (2011).

 

Motion correction

  • J. Moore, J. Jimenez, W. Lin, W. Powers, X. Zong, Prospective Motion Correction and Automatic Segmentation of Penetrating Arteries in Phase Contrast MRI at 7 T, Magn. Reson. Med, doi: 10.1002/mrm.29364 (2022).

  • X. Zong, S. Nanavati, T. Li, and W. Lin, Effects of Motion and Retrospective Motion Correction on the Visualization and Quantification of Perivascular Spaces in Ultrahigh Resolution T2-weighted Images at 7 T, Magn. Reson. Med. 86, 1944 (2021).

 

Small vessel segmentation and quantification

  • X. Zong and W. Lin, Quantitative Phase Contrast MRI of Penetrating Arteries in Centrum Semiovale at 7T, NeuroImage 195, 463-474 (2019).

  • X. Zong, C. Lian, J. Jimenez, K. Yamashita, D. Shen, W. Lin, Morphology of perivascular spaces and enclosed blood vessels in young to middle-aged healthy adults at 7T: Dependences on age, brain region, and breathing gas, Neuroimage 218, 116978, (2020).






实验室介绍:

The Brain MRI physics lab focuses on the following research areas:

  • Illuminating physical and physiological underpinnings of brain MRI contrasts: we aim to illuminate the physical and physiological mechanisms of MRI contrasts (such as fMRI, CEST, Phase contrast, DTI, SWI, T2 hyperintensities, and enlarged perivascular spaces) in order to facilitate more scientific and efficient use of MRI in clinical research, disease diagnosis and treatment.

  • Developing new MRI techniques: Based on the practical needs of research and clinical applications, developing more convenient, efficient, and robust MRI methods via sequence design, sequence parameter optimization, and motion correction.

  • Illuminating the etiology of neurological disorders and developing early diagnosis and efficient treatment strategies: Ultimately, we will combine the developed MRI techniques with other measurements such as body fluid measurements, behavioral and cognitive assessments, clinical trials, animal models, and AI-based data analysis, to systematically study the etiopathogenesis of neurological disorders and to develop early diagnosis and efficient treatment strategies.