Zhong Lab

 

The Zhong Lab is part of Magnetic Resonance Research Labs (MRRL) in the Department of Radiological Sciences within the David Geffen School of Medicine at UCLA. We aspire to further our understanding of human cardiovascular system and improve clinical diagnosis and treatment through development and translation of state-of-art technologies. Using Magnetic Resonance (MR) imaging as the primary modality, we develop advanced MR imaging techniques, image reconstruction algorithms, data analysis methods, deep learning networks and computational models to achieve our goal. We collaborate with physician researchers and industrial partners to steer our translational research, so that we better understand cardiovascular function and pathophysiology, and improve clinical diagnosis, treatment and monitoring of various clinical conditions including congenital heart diseases and acquired cardiovascular diseases. Our techniques also extend to applications of other organs such as the liver and skeletal muscle.

 

 

We Are Looking For

We have the following potential openings in our lab. If you are interested in learning more details and applying, please email: xiaodongzhong@mednet.ucla.edu 

• Graduate student, including Ph.D. student, M.D./Ph.D. student and M.S. student
• Postdoc scholar
• Volunteer/rotating/visiting researcher, including undergraduate student, graduate (M.S. or Ph.D.) student and postdoc scholar

 

 

Research Areas

Technical Developments

• MR pulse sequence design
• Fast imaging techniques
• Image reconstruction algorithms
• Quantitative imaging techniques
• Artificial intelligence / deep learning networks

 

Research Directions

• Cardiovascular diseases 
• Strain imaging / Tissue mechanics
• Flow imaging / Fluid mechanics
• Fat and iron quantification

 

 

Lab Members

Faculty

        Xiaodong Zhong, Ph.D.

        Shu-Fu Shih, Ph.D. (jointly supervised with Dr. Holden Wu)

 

        Siyue Li, Ph.D.

 

        Sile Wang, M.S.

        Yuxiao Wu, B.S.

 

        Heather Wilbur

 

 

Selected Publications

• Zhong X, Nickel MD, Kannengiesser SAR, Dale BM, Han F, Gao C, Shih SF, Dai Q, Curiel O, Tsao TC, Wu HH, Deshpande V. Accelerated free-breathing liver fat and R2* quantification using multi-echo stack-of-radial MRI with motion-resolved multidimensional regularized reconstruction: Initial retrospective evaluation. Magn Reson Med 2024;92:1149-1161.
• Gao C, Ming Z, Nguyen KL, Pang J, Bedayat A, Dale BM, Zhong X, Finn JP. Ferumoxytol-enhanced cardiac cine MRI reconstruction using a variable-splitting spatiotemporal network. J Magn Reson Imaging 2024. doi: 10.1002/jmri.29295. Epub ahead of print.
• Gao C, Ghodrati V, Shih SF, Wu HH, Liu Y, Nickel MD, Vahle T, Dale B, Sai V, Felker E, Surawech C, Miao Q, Finn JP, Zhong X, Hu P. Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network. Magn Reson Imaging 2023;95:70-79.
• Zhong X, Armstrong T, Gao C, Nickel MD, Han F, Dale BM, Li X, Kafali SG, Hu P, Wu HH, Deshpande V. Accelerated k-space shift calibration for free-breathing stack-of-radial MRI quantification of liver fat and R2*. Magn Reson Med 2022;87:281-291.
• Gao C, Shih SF, Finn JP, Zhong X. A projection-based k-space transformer network for undersampled radial MRI reconstruction with limited training subjects. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2022;17:726-736.
• Zhong X, Hu HH, Armstrong T, Li X, Lee YH, Tsao TC, Nickel MD, Kannengiesser SAR, Dale BM, Deshpande V, Kiefer B, Wu HH. Free-breathing volumetric liver R2* and proton density fat fraction quantification in pediatric patients using stack-of-radial MRI with self-gating motion compensation. J Magn Reson Imaging 2021;53:118-129. (Editorial for this paper by Mozes FE: J Magn Reson Imaging 2021;53:130-131.)
• Zhong X, Armstrong T, Nickel MD, Kannengiesser SAR, Pan L, Dale BM, Deshpande V, Kiefer B, Wu HH. Effect of respiratory motion on free-breathing 3D stack-of-radial liver R2* relaxometry and improved quantification accuracy using self-gating. Magn Reson Med 2020;83:1964-1978.
• Zhong X, Nickel MD, Kannengiesser SAR, Dale BM, Kiefer B, Bashir MR. Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging. Magn Reson Med 2014;72:1353-1365.
• Zhong X, Gibberman LB, Spottiswoode BS, Gilliam AD, Meyer CH, French BA, Epstein FH. Comprehensive cardiovascular magnetic resonance of myocardial mechanics in mice using three-dimensional cine DENSE. J Cardiovasc Magn Reson 2011;13:83.
• Zhong X, Spottiswoode BS, Meyer CH, Kramer CM, Epstein FH. Imaging three-dimensional myocardial mechanics using navigator-gated volumetric spiral cine DENSE MRI. Magn Reson Med 2010;64:1089-1097.

 

Selected Patents / Invention Disclosures

• Zhong X, Nickel MD, Kannengiesser SAR, Deshpande V. Methods, apparatuses, systems, and computer-readable mediums for biomarker quantification using free-breathing stack-of-radial imaging. US20240331225 A1. 2024.
• Gao C, Finn J, Zhong X. Method and apparatus for accelerated acquisition and artifact reduction of undersampled MRI using a k-space transformer network. US20230342993 A1. 2023.
• Ghodrati V, Gao C, Hu P, Zhong X, Wetzl J, Pang J. Method and apparatus for accelerated acquisition and reconstruction of cine MRI using a deep learning based convolutional neuro network. US20240037815 A1. 2024.
• Hu P, Zhong X, Ghodrati V, Gao C. Method and system for accelerated acquisition and artifact reduction of undersampled MRI using a deep learning based 3D generative adversarial network. US20220381861 A1. 2022.
• Cai X, Epstein FH, Zhong X. System of free-breathing cine DENSE MRI using image-based self-navigation. US10310047 B2. 2019.
• Zhong X, Qiu D, Oshinski J, Saindane A. Magnetic resonance method and apparatus for quantitative simultaneous multi-slice assessment of tissue displacement, deformation, and related biomarker parameters. US10054653 B2. 2018.
• Zhong X, Nickel MD, Kannengiesser SAR. MR fat and iron quantification using a multi-step adaptive fitting approach with multi-echo magnetic resonance imaging. US20140126795 A1. 2015.