Jun Miao

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Jun Miao
PET Imaging Scientist at GE Healthcare
Summary
# Research scientist with broad experience in all aspects of MR imaging physics, imaging system optimization,
reconstruction algorithm, image processing and analysis; CT reconstruction algorithm, FOV truncation
completion, low dose and lesion detectability; PET physics and correction algorithms, iterative reconstruction
and low contrast lesion detectability. # Substantial in-house experiences on implementing and developing fast
MR imaging reconstruction algorithms for clinical MR scanners, # Experience in Monte Carlo simulation
for radiation particle tracking. Specialties: MRI physics, system and pulse sequence, MR image processing,
medical imaging system optimization (MR, CT, PET), medical image quality, and human vision model.
Programming skills and scientific software usability include C/C++, Matlab, Fortran, PYTHIA, ALPGEN,
GEANT.
Experience
PET System Scientist – Physics and Image Quality at GE Healthcare
April 2012 – Present (3 years 11 months)
• Image quality lead for new PET/CT product Discovery IQ • Advanced PET and CT image reconstruction
algorithms, development and assessment • PET and CT image registration, image truncation completion
algorithms • Subjective and objective image quality, low contrast lesion detection using numerical observer
Imaging Physics Researcher at Philips Medical Systems
August 2010 – February 2012 (1 year 7 months)
# MRI system and pulse sequence, RF coils, shimming techniques, multi-nuclear spectroscopy, 2D chemical
shift imaging (CSI) # PET/MR system analysis, attenuation correction using MR, validation of PET/MR
using PET/CT # Low-dose CT, iterative reconstruction, image quality, lesion detection
Research Assistant at Department of Biomedical Engineering, Case Western Reserve University
August 2005 – February 2012 (6 years 7 months)
# Human visual perceptual difference model (Case-PDM) # Image quality, diagnosis relevant image quality,
human observer experiments, and imaging system optimization # Novel partially parallel MR imaging
techniques # Fast dynamic MR imaging (DCE-MR) # Fast diffusion weighted imaging # Sparse MRI
(novel compressed sensing method and its combination with parallel MR imaging) # MR motion correction
# Image quality controlled k-space sampling trajectory design # Image analysis in chemical shift imaging
(fat/water separation) and Diffusion Tensor imaging (DTI) # T1 mapping # Computed tomography
reconstruction
MRI Algorithm Intern at Siemens Corporate Research
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May 2009 – August 2009 (4 months)
# Implementing and developing fast MR imaging reconstruction algorithms for clinical MR scanners, in
particular, acceleration of parallel MR imaging with compressed sensing. # GRAPPA reconstruction
technique optimization # Two pending US patents for novel fast MR imaging techniques.
Research Assistant at Fermi National Accelerator Laboratory
May 2003 – August 2005 (2 years 4 months)
# Radiation particle / high energy physics # Monte Carlo radiation particle tracking applications (PYTHIA,
ALPGEN, GEANT, etc) # Searching for new particles like lepto-quark using both Monte Carlo simulation
and Tevatron Collider Run II data at energy level of 1.96 TeV (particle trajectory/energy reconstruction, data
analysis, etc.) # Electronic chip SUV4 testing/repairing for the collider detector # Mother board testing for
the calorimeter # DAQ and Calorimeter shifter at DZero Fermilab
Technical Engineer at Applied Physics Institute, Guangxi Science Acedemy, China
July 1999 – August 2001 (2 years 2 months)
# Thermodynamics modeling and energy efficiency study for both civil building and solar house (i.e. an
independent environment-friendly green building) # Solar cell array application in solar house # Marketing
and sales of solar thermal units for civil house application
Skills & Expertise
Image Processing
Matlab
Algorithms
MRI
PET
Medical Imaging
C++
Tomography
Biomedical Engineering
Physics
Digital Imaging
Simulations
Spectroscopy
Image Analysis
Signal Processing
Medical Devices
R&D
Research
Testing
Programming
Data Analysis
Electronics
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Computed Tomography
LabVIEW
Numerical Analysis
Patents
Compressed Sensing Reconstruction for Equidistant K-Space and Application in Parallel MR Imaging
United States Patent Application 2009P18434US
Inventors: Jun M.
Education
Case Western Reserve University
Doctor of Philosophy (PhD), Biomedical Imaging, 2005 – 2011
Activities and Societies: SPIE, ISMRM, AAPM, RSNA
Florida State University
MSc, Physics, 2002 – 2005
Activities and Societies: American Society of Physics
Honors and Awards
# 2007 Michael B. Merickel Best Student Paper Award Finalist, SPIE Medical Imaging Conference, San Diego,
California # 2009 Student Traveling Award, ISMRM Workshop on Data Sampling and Image Reconstruction,
Sedona, Arizona # 2009 Cum Laude Poster Award, SPIE Medical Imaging Conference, Orlando, Florida #
2010 Winner of SPIE Scholarship (International) in Optical Science and Engineering
Publications
Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative
difference penalties of clinical PET
Physics in Medicine and Biology July 1, 2015
Authors: Jun M., Sangtae A., Steve R., Xiao Jin, Scott Wollenweber, Chuck Stearns
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Jun Miao
PET Imaging Scientist at GE Healthcare
Contact Jun on LinkedIn