Hello, I am Fu Li, a Ph.D candidate from Compuatational Imaging Science Lab at University of Illinois Urbana-Champaign. I am supervised by Dr. Mark A. Anastasio and Dr. Umberto Villa. I earned my BSc degree in Computational Mathematics from Sun Yat-sen University, China.
I have rich experience in numerical modeling, optimization, tomographic imaging, and deep learning for medical imaging applications. My Ph.D. research focuses on developing advanced PDE-constrained optimization problems for high-resolution imaging using ultrasound.
Research Interest: Computational imaging, Medical imaging, Full waveform inversion, Deep learning.
Work Experience
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CT research intern at Canon Medical Research, USA, working on photon counting CT, learning-based image denoising, and reconstruction during the summer of 2024.
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Algorithm Engineer at PVmed, working on medical imaging processing, clinical data anaylsis from 2016 to 2018
Selected Publications
(2023) A Forward Model Incorporating Elevation-Focused Transducer Properties for 3-D Full-Waveform Inversion in Ultrasound Computed Tomography
Fu Li, Umberto Villa, Nebojsa Duric, Mark A. Anastasio. IEEE Transactions on UFFC. [PDF].
(2021) 3-D Stochastic Numerical Breast Phantoms for Enabling Virtual Imaging Trials of Ultrasound Computed Tomography
Fu Li, Umberto Villa, Seonyeong Park, Mark A. Anastasio. IEEE Transactions on UFFC. [PDF].
(2023) Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer
Seonyeong Pirk, Umberto Villa, Fu Li, Refik Mert Cam, Alexander A. Oraevsky, Mark A. Anastasio. Journal of Biomedical Optics. [Link].
(2023) 3D full-waveform inversion in ultrasound computed tomography employing a ring-array
Fu Li, Umberto Villa, Neb Duric, Mark A. Anastasio. SPIE medical imaging. [PDF].
(2023) Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
Luke Lozenski, Hanchen Wang, Fu Li, Mark A. Anastasio, Brendt Wohlberg, Youzuo Lin, Umberto Villa. IEEE Transactions on computational imaging. [PDF].
(2023) Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography
Gangwon Jeong, Fu Li, Umberto Villa, Mark A. Anastasio. Arxiv. [PDF].
Conference Presentations & Invited Seminars
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Advanced image reconstruction for accurate and high-resolution breast ultrasound tomography. Bioengineering Distinguished Seminar Series, UIUC, Urbana, 2023
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Three-dimensional time-domain full-waveform inversion for ring-array-based ultrasound computed tomography. 184th Acoustic society meeting, Chicago, 2023.
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Automatic Gross Tumor Volume Delineation for Nasopharyngeal Carcinoma Radiotherapy on Multi-modal MRI: A Deep Learning Model Trained from 1000 Patient Dataset. RSNA, Chicago, 2018.
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Prediction of Clinical Target Volume for Nasopharyngeal Carcinoma Using Hidden Markov Model Trained from 2000 Patient Dataset. RSNA, Chicago, 2017.
Academic Service
- Journal reviewer: IEEE T-UFFC, Medical physics, Photoacoustics
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