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Hung P. Do, PhD, MSEE | resume

Hung P. Do, PhD, MSEE

Senior MRI Physicist, Canon Medical Systems USA

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Expertise and Skills

  • Magnetic Resonance Imaging (MRI) Physics, MRI Pulse Sequence Design, and Advanced Image Reconstruction
  • Signal and Image Processing
  • Design and execute scientific, FDA 510(k)-cleared, and hypothesis-driven research studies
  • Applications of Statistics, Data Science, Machine Learning, and Deep Learning to MRI, Medical Imaging, and Healthcare
  • Clinical evaluations and translations of innovative imaging solutions into clinical practice
  • End-to-end project management and multidisciplinary collaborations
  • Fundamental and translational MRI research related to quantitative imaging, novel pulse sequence design, and advanced image reconstruction
  • Programmable animation, data visualization, data analysis, and statistics using Python and R
  • Bayesian methods and probabilistic programming for quantitative imaging with uncertainty quantification
  • Mathematical modeling, numerical simulations, and optimization
  • Automatic and reproducible pipelines for data curation, cleaning, and visualization, statistical analysis, and report/presentation generation using Git version control, Bash, Python, R, and related packages
  • Operating Systems: Linux, macOS, and Microsoft Windows
  • Programming Languages: Python, R, Matlab, Bash, EPIC (General Electric (GE) Healthcare's C-based MRI pulse sequence programming language)
  • Packages and Libraries: Pandas, NumPy, PyDicom, SciPy, Matplotlib, Tidyverse, ggplot2, Seaborn, fastai, TensorFlow-Keras, PyTorch, nbdev
  • Version Control: Git and GitHub
  • Package and Environments Management: Anaconda and PyPI's pip
  • Tools: Microsoft Office, Command Line (Terminal), Markdown, HTML, CSS, \( \LaTeX \), OsiriX, Visual Studio Code


Experience


Education


Honors and Awards


Certificates

  • Neural Networks for Machine Learning, Prof. Geoffrey E. Hinton, University of Toronto
  • Deep Learning, a 5-Course Specialization, Prof. Andrew Ng, deeplearning.ai and Stanford University
    • Course 1: Neural Networks and Deep Learning
    • Course 2: Improving Deep Neural Networks: Hyper-parameter tuning, Regularization and Optimization
    • Course 3: Structuring Machine Learning Projects
    • Course 4: Convolutional Neural Networks
    • Course 5: Sequence Models
  • Machine Learning, Prof. Andrew Ng, Stanford University
  • AI for Healthcare, a 5-Course Specialization, Profs. Nigam Shah, Laurence Baker, David Magnus, Serena Yeung, Mildred Cho, Steven Bagley, Matthew Lungren, Tina Hernandez-Boussard, Stanford University
    • Course 1: Introduction to Healthcare
    • Course 2: Introduction to Clinical Data
    • Course 3: Fundamentals of Machine Learning for Healthcare
    • Course 4: Evaluations of AI applications in Healthcare
    • Course 5: AI in Healthcare Capstone Projects
  • AI for Medicine, a 3-Course Specialization, Pranav Rajpurkar PhD, Stanford University and deeplerning.ai
    • Course 1: AI for Medical Diagnosis
    • Course 2: AI for Medical Prognosis
    • Course 3: AI for Medical Treatment
  • Statistics with Python, a 3-Course Specialization, Brenda Gunderson PhD, Brady T. West PhD, Kerby Shedden PhD, University of Michigan
    • Course 1: Understanding and Visualizing Data with Python
    • Course 2: Inferential Statistical Analysis with Python
    • Course 3: Fitting Statistical Models to Data with Python
  • Data Science: Statistics and Machine Learning, a 5-Course Specialization, Profs. Jeff Leek, Roger Peng, Brian Caffo, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University
    • Course 1: Statistical Inference
    • Course 2: Regression Model
    • Course 3: Practical Machine Learning
    • Course 4: Developing Data Products
    • Course 5: Data Science Capstone Project
  • Data Science: Foundations using R, a 5-Course Specialization, Profs. Jeff Leek, Roger Peng, Brian Caffo, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University
    • Course 1: The Data Scientist’s Toolbox
    • Course 2: R Programming
    • Course 3: Getting and Cleaning Data
    • Course 4: Exploratory Data Analysis
    • Course 5: Reproducible Research
  • Improving your statistical inferences, Daniel Lakens PhD, Eindhoven University of Technology
  • Improving Your Statistical Questions, by Daniel Lakens PhD, Eindhoven University of Technology


Selected Publications

Full list of publications can be seen at Publications

Selected Peer-reviewed Journal Papers
  • J Starekova, D Rutkowski, W Bae, HP Do, A Madhuranthakam, V Malis, S Lin, S Serai, T Yokoo, SB Reeder, JH Brittain, and D Hernando. "Multi‐Center, Multi‐Vendor Validation of Simultaneous MRI‐Based Proton Density Fat Fraction and R2* Mapping Using a Combined Proton Density Fat Fraction‐R2* Phantom." Journal of Magnetic Resonance Imaging 2025. PDF JRNL
  • HP Do, CA Lockard, D Berkeley, B Tymkiw, N Dulude, S Tashman, G Gold, J Gross, E Kelly, and CP Ho. “Improved Resolution and Image Quality of Musculoskeletal Magnetic Resonance Imaging using Deep Learning-based Denoising Reconstruction: A Prospective Clinical Study.” Skeletal Radiology 2024. PDF JRNL
  • HP Do, Y Guo, AJ Yoon, and KS Nayak. “Accuracy, Uncertainty, and Adaptability of Automatic Myocardial ASL Segmentation using Deep CNN.” Magnetic Resonance in Medicine 2020; 83:1863–1874. PDF JRNL
  • HP Do, V Ramanan, X Qui, J Barry, GA Wright, NR Ghugre, KS Nayak. “Non-Contrast Assessment of Microvascular Integrity using Arterial Spin Labeled CMR in a Porcine Model of Acute Myocardial Infarction.” Journal of Cardiovascular Magnetic Resonance 20:45, July 2018. PDF JRNL
  • HP Do, AJ Yoon, MW Fong, F Saremi, ML Barr, KS Nayak. “Double-gated Myocardial Arterial Spin Labeled Perfusion Imaging is Robust to Heart Rate Variation.” Magnetic Resonance in Medicine 77(5):1975-1980, 2017. PDF JRNL


Selected Whitepapers
  • Do, Hung P. and Berkeley, Dawn. “Advanced intelligent Clear-IQ Engine (AiCE) Deep Learning Reconstruction (DLR): Translating the Power of Deep Learning to MR Image Reconstruction.” Canon Medical Systems USA, 2020. PDF
  • Do, Hung P. “Advanced intelligent Clear-IQ Engine (AiCE) Interpretable Model with Robust and Generalized Performance: Beyond Brain and Knee.” Canon Medical Systems USA, 2021. PDF
  • Do, Hung P. “k-t SPEEDER: A reference-free parallel imaging method for fast dynamic MRI.” Canon Medical Systems USA, 2019. PDF


Selected Talks

Full list of talks can be seen at Talks

  • HP Do, et al. "Accelerated 1.2-minute 4-echo UTE-based CT-like Imaging using CG-SENSE and Deep Learning-based Denoising Reconstruction (DLR).” International Society for Magnetic Resonance in Medicine Scientific Meeting, Hawaii, May 2025. SLIDES-PDF VIDEO-YouTube
  • HP Do, et al. "Accelerated 2-3-Minute Multi-echo Ultra-short Echo Time (mecho UTE) using Conjugate Gradient SENSE (CG-SENSE) Reconstruction.” The Radiological Society of North America (RSNA) Scientific Session, Chicago, Nov 2023. SLIDES-PDF VIDEO-YouTube
  • HP Do, et al. "Eleven-minute Comprehensive MSK Imaging Using Deep Learning Reconstruction (DLR) and Multi-echo Ultrashort Echo-Time (UTE)." The Radiological Society of North America (RSNA) Scientific Session, Chicago, Nov 2022. SLIDES-PDF
  • HP Do, et al. "dnoiseNET: Deep Convolutional Neural Network for Image Denoising." The ISMRM & SCMR Co-Provided Workshop on the Emerging Role of Machine Learning in Cardiovascular Magnetic Resonance Imaging, Seattle, Feb 2019. SLIDES-PDF
  • HP Do, et al. "Deep Convolutional Neural Network for Segmentation of Myocardial ASL Short-Axis Data: Accuracy, Uncertainty, and Adaptability." The ISMRM Workshop on Machine Learning, Part II, Washington D.C., Oct 2018. SLIDES-PDF VIDEO-YouTube


Teaching and Training Experience


Peer Reviewer


Membership


Interests

  • Jogging, Hiking, Nature Walk
  • Programming, Science, Technology
  • Update: I’m starting a personal blog where I share “cool stuff”, ideas and insights at the intersection of MRI physics, mathematics, and innovation. With over 8 years of experience training sales teams on MRI physics and the latest MRI technologies, my goal is to make technical concepts that were once inaccessible feel clear, engaging, and enjoyable. If this sounds interesting to you, the best way to stay informed about future posts is to subscribe to the newsletter below and receive new posts directly in your inbox.



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