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 developments of novel imaging methods that target specific clinical and research applications. Our current research projects include:

Clinical Applications

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

Technical Developments

Machine Learning / Deep Learning Algorithms
Compressed Sensing MRI Algorithms
Quantitative Image Analysis / Modeling
Rapid / Real-Time Imaging

Media Coverage / Awards / Invited Lectures

Human Placenta Project - Media Release
Placenta Arterial Spin Labeling - Media Release
Artificial Intelligence in Prostate MRI - 10th MAGNETOM World Summit
Placenta Multi-parametric MRI - 2018 NIH Human Placenta Project (from 1:03:00)
2019 IEEE International Symposium on Biomedical Imaging (ISBI) - Runners-up Best Paper Award | News Coverage
Multi-parametric MRI for Prostate Cancer - News Coverage
Artificial Intelligence in Prostate MRI - Media Release
Improved Prostate Cancer Diagnosis - Daily Bruin
Placenta Arterial Spin Labeling MRI - NICHD News | News Coverage | JMRI Editorial
AI Applications in Medical Imaging - RSNA News
NIH National Cancer Institute Award - UCLA Newsroom | The Cancer Letter
Nature - How AI is improving cancer diagnosis
Blood test to predict pregnancy complications - UCLA Newsroom

Lab Members:

Faculty:
Kyung Sung, Ph.D.

Project Scientists / Postdoctoral Scholars:
Ida Sonni, M.D.
Fatemeh Zabihollahy, Ph.D.

Ph.D. / Master's Students:
     Jiahao Lin, M.S.
     Alibek Danyalov, B.S.
     Yongkai Liu, M.S.
     Haoxin Zheng, M.S.
     Ran Yan, M.S.
     Alex Hung, M.S.

Medical Students / Clinical Fellows:
Brian Lee, M.D.
Arya R Alibadi, M.D. Candidate

Administrative Staffs:
Heather Wilbur
Stephanie Peterson

Study Coordinators:
     Victoria Rueda
     Tammy Floore
     Niloofar Sheshebor

Previous Members:
     Novena Rangwala, Ph.D. – Postdoctoral Fellow, 2013 — 2016 (Currently: GE Healthcare)
     Dapeng Liu, Ph.D. – Postdoctoral Fellow, 2016 — 2017 (Currently: Dept. of Radiology, Johns Hopkins University)
     Yeejin Lee, Ph.D. – Postdoctoral Fellow, 2017 — 2018 (Currently: Dept. of Electrical Engineering, Seoul National University of Science and Technology)
     Thomas Martin, Ph.D – Ph.D. Student, 2012 — 2018 (Currently: Dept. of Radiation Oncology, Univ. of Texas at San Antonio)
     Xinran Zhong, Ph.D. - Ph.D Student, 2014 — 2019 (Currently: Dept. of Radiation Oncology, Univ. of Texas Southwestern)
     Ruiming Cao, M.S. - Masters Student, 2017 — 2019 (Currently: Dept. of Bioengineering, UC Berkeley)
     Eun Bin (Eunice) Lee, M.S. - Masters Student, 2017 — 2019,

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 detail.

Publications:

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
2019-2020 Tanner Project Foundation (PI: Sung): Mining the Dark Data of Breast Imaging using Artificial Intelligence to Predict Data Patterns
2015-2020 NIH: U01 HD087221 (PI: Devaskar/Janzen/Sung): Imaging Innovations for Placental Assessment in Response to Environmental Pollution
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
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