Fang Liu is the Director of the Intelligent Imaging Innovation and Translation Lab at Athinoula A. Martinos Center for Biomedical Imaging and Assistant Professor of Radiology at Harvard Medical School. His research focuses on medical image acquisition and reconstruction, image analysis and processing, and physiological modeling of magnetic resonance, molecular, and optical imaging. His interest also includes the development of artificial intelligence methods for improving imaging speed and robustness and automating clinical imaging workflow.
The research at Dr. Liu’s lab centers on three specific areas:
1) Develop artificial intelligence/machine learning techniques to design, optimize and accelerate image acquisition and reconstruction of quantitative, multi-parametric, and dynamic MRI.
2) Develop and optimize novel MRI pulse sequences and acquisition methods for rapid and robust imaging of mesoscale tissue structure, composition, and function.
3) Develop intelligent methods and software solutions for medical signal and image data analysis and processing in translational imaging research.
Before joining Harvard in 2020, Dr. Liu was an Assistant Scientist at Radiology, University of Wisconsin-Madison, from 2015 to 2019, working on translational imaging projects spanning several clinical topics in the brain, body, and musculoskeletal systems. In 2015, he received PhD in Medical Physics from the University of Wisconsin-Madison. His research focused on developing new MR pulse sequences and optimizing imaging biomarkers for improved musculoskeletal and neural tissue assessment. In 2011, he received MSc in Medical Biophysics from Western University, Canada, where his research focused on improving breast MR imaging using machine learning. In 2008, he received BSc in Biomedical Engineering from Sun Yat-sen University, China.
Education
PhD in Medical Physics, University of Wisconsin-Madison
Select Publications
1. Liu F, Zhou Z, Jang H, Zhao G, Samsonov A, Kijowski R: Deep Convolutional Neural Network and 3D Deformable Approach for Tissue Segmentation in Musculoskeletal Magnetic Resonance Imaging. Magn Reson Med. 2017; 79 (4), 2379-2391.
2. Liu F, Zhou Z, Blankenbaker D, Larison W, Kanarek A, Lian K, Kambhampati S, Kijowski R: Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection. Radiology. 2018; 289 (1), 160-169.
3. Liu F, Kijowski R, El Fakhri G, Feng L: Magnetic Resonance Parameter Mapping Using Model-guided Self-supervised Deep Learning. Magn Reson Med. 2021; 85 (6), 3211-3226.
Highlights
2021 – ISMRM Annual Meeting Program Committee
2018 – ISMRM Junior Fellow
2016 – OCSMRM Young Investigator Award