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Sung Lab

The Sung Lab, directed by Kyung Sung, is part of Magnetic Resonance Research Labs (MRRL) in the Department of Radiological Sciences within the School of Medicine at UCLA. Our mission is to advance state-of-the-art diagnostic imaging using magnetic resonance imaging (MRI). Our research focuses on the development of novel imaging methods that target specific clinical and research applications. Our current research projects include:

Clinical Applications

  • Quantitative Multi-parametric MRI for Prostate Cancer 
  • Multi-Parametric Human Placenta Imaging 
  • Liver Perfusion MRI
  • Quantitative Breast Perfusion MRI
  • Pituitary Microadenoma Imaging with a Deployable Microcoil

Technical Developments

  • Machine Learning / Deep Learning Algorithms 
  • Advanced Image Reconstruction
  • Quantitative Image Analysis / Modeling
  • Rapid / Real-Time Imaging

Media Coverage / Awards / Invited Lectures

Lab Members:

Kyung Sung, Ph.D.    
Project Scientists / Postdoctoral Scholars:
Kai Zhao, Ph.D.    
Ph.D. / Master's Students:
Haoxin Zheng, M.S. Ran Yan, M.S.
Alex Hung, M.S. Sohaib Naim, M.S.
Kaifeng Pang Raymi Ramirez
Katarina Chiam    
Clinical Fellows / Medical Students / Undergraduate Students:
Daniel H. Kim, M.D. Candidate Vishnu Murthy, M.D. Candidate
Brian Chau    
Administrative Staffs:    
Heather Wilbur    
Study Coordinators:    
Nashla Barroso Victoria Rueda


Previous Members:
     Novena Rangwala, Ph.D. – Postdoctoral Fellow, 2013 —  2016 (Currently: GE Healthcare)
     Dapeng Liu, Ph.D. – Postdoctoral Fellow, 2016 —  2017 (Currently: Research Associate, Dept. of Radiology, Johns Hopkins University)
     Yeejin Lee, Ph.D. – Postdoctoral Fellow, 2017 —  2018 (Currently: Associate Professor, Dept. of Electrical and Information Engineering, Seoul National University of Science and Technology)
     Thomas Martin, Ph.D. – Ph.D. Student, 2012 —  2018 (Currently: Assistant Professor, Dept. of Radiation Oncology, University of Utah and a Medical Physicist at Huntsman Cancer Hospital)
     Xinran Zhong, Ph.D. - Ph.D. Student, 2014 —  2019 (Currently: Assistant Professor, Dept. of Radiation Oncology, Univ. of Texas Southwestern)
     Ruiming Cao, M.S. – Masters Student, 2017 —  2019 (Currently: Ph.D. Student, Dept. of Bioengineering, UC Berkeley)
     Yongkai Liu, Ph.D. – Ph.D. Student, 2017—  2022 (Currently: Postdoctoral Scholar, Dept. of Radiology, Stanford)
     Jiahao Lin, Ph.D. – Ph.D. Student, 2016—  2023 (Currently: Meta Platforms (META), Inc.)

Previous Visitors:
     Duanduan (Sally) Liu – Visiting Graduate Student, Feb 2013 — July 2013
     Eric Cho – Visiting Student, May 2013 — July 2013
     ByungJoo (Joshua) Park, Ph.D. – Visiting Scholar, Feb 2013 — Feb 2014
     Wenrui Yang – Visiting Undergraduate Student, Sep 2013 — Mar 2014
     Marina Miranda – Summer Research Student, Brazil Scientific Mobility Program (BSMP), May 2016 — Aug 2016
     Tianle Cao – Summer Research Student, Tsinghua University, June 2017 — Sep 2017
     Kang-Sun Choi, Ph.D. – Visiting Scholar, Jan 2017 — Jan 2018

     Wakana Murakami,  M.D.– Visiting Scholar, Sep 2020 — Nov 2021

We are seeking an excellent postdoctoral fellow/graduate student with a strong background in signal and image processing, with an interest in developing novel MRI methods. Please see here for more details.


Published Manuscripts @Pubmed or @Google Scholar

Research Funding:

  • 2020-2025 NIH: R01 CA248506 (PI: Sung/Wu): Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
  • 2022-2024 NIH: R01 CA248506-S1 (PI: Sung/Wu): A Structured Multi-scale Dataset with Prostate MRI for AI/ML Research
  • 2023-2028 NIH: R01 CA272702 (PI: Sung/Kim): Racially-associated MRI Analysis and Modeling for Predicting Aggressive Prostate Cancer
  • 2019-2024 NIH: R01 HD10015 (PI: Devaskar): Electrochemical Liquid Biopsy Assessing Placenta Health
  • 2020-2024 NIH: R01 DK124417 (PI: Wu/Calkins): Quantifying Body Composition and Liver Disease in Children Using Free-Breathing MRI and MRE
  • 2020-2022 Tate Fund (PI: Lee-Felker/Sung): Combined Conventional and Ultrafast MRI-Based Integrated Radiomic Models for Predicting Response to Neoadjuvant Chemotherapy in Women with Triple Negative Breast Cancer