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Ruoqiao Zhang

from Vernon Hills, IL
Age ~37
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Related to:
Peng C ZhangXiaowei ZhangLu ZhangLei Zhang, 57Yang Zhang, 38Yuanfang Zhang, 47Xuejing Zhang, 40
Connected to:
Aiguang Zhang, 35Aiteen Zhang, 58Alexandria Zhang, 37Aoxiang Zhang, 39Banghai Zhang, 51Baolian Zhang, 74Bartholomew Zhang, 28Bingfan Zhang, 63Boche Zhang, 32Bochuan Zhang, 33

Ruoqiao Zhang Phones & Addresses

  • Vernon Hills, IL
  • Seattle, WA
  • Bellevue, WA
  • West Lafayette, IN
  • Waukesha, WI
  • W Lafayette, IN
  • Arlington Heights, IL

Work

Company: Canon medical research usa, inc. Aug 2017 Position: Senior scientist

Education

Degree: Doctorates, Doctor of Philosophy School / High School: Purdue University 2009 to 2015 Specialities: Electrical Engineering, Computer Engineering, Philosophy

Skills

Matlab • Image Processing • Latex • Signal Processing • Algorithms • Statistics • Medical Imaging • C • C++ • Computer Vision • Physics • Simulations • Computed Tomography

Industries

Research

Resumes

Resumes

Ruoqiao Zhang Photo 1

Senior Scientist

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Location:
Chicago, IL
Industry:
Research
Work:
Canon Medical Research Usa, Inc.
Senior Scientist
University of Washington Dec 2015 - Aug 2017
Postdoctoral Fellow
Purdue University Aug 2009 - Nov 2015
Research Assistant
Ge Healthcare May 2011 - Aug 2014
Research Intern
Education:
Purdue University 2009 - 2015
Doctorates, Doctor of Philosophy, Electrical Engineering, Computer Engineering, Philosophy Tsinghua University 2005 - 2009
Bachelors, Bachelor of Science, Engineering, Physics
Skills:
Matlab
Image Processing
Latex
Signal Processing
Algorithms
Statistics
Medical Imaging
C
C++
Computer Vision
Physics
Simulations
Computed Tomography

Publications

Us Patents

Methods And Systems For Performing Model-Based Iterative Reconstruction

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US Patent:
20130343624, Dec 26, 2013
Filed:
Jun 22, 2012
Appl. No.:
13/530846
Inventors:
JEAN-BAPTISTE THIBAULT - WAUKESHA WI, US
RUOQIAO ZHANG - LAFAYETTE IN, US
JIANG HSIEH - BROOKFIELD WI, US
KEN DAVID SAUER - SOUTH BEND IN, US
Assignee:
General Electric Company - Schenectady NY
International Classification:
G06K 9/36
US Classification:
382131
Abstract:
A method for reconstructing image component densities of an object includes acquiring multi-spectral x-ray tomographic data, performing a material decomposition of the multi-spectral x-ray tomographic data to generate a plurality of material sinograms, and reconstructing a plurality of material component density images by iteratively optimizing a functional that includes a joint likelihood term of at least two of the material decomposed sinograms. An x-ray tomography imaging system and a non-transitory computer readable medium are also described herein.

Projection Based Deep Learning With Frequency Splitting For Computed Tomography

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US Patent:
20230067596, Mar 2, 2023
Filed:
Aug 31, 2021
Appl. No.:
17/462391
Inventors:
- Otawara-shi, JP
Ruoqiao ZHANG - Vernon Hills IL, US
Jian ZHOU - Vernon Hills IL, US
Zhou YU - Vernon Hills IL, US
Assignee:
CANON MEDICAL SYSTEMS CORPORATION - Otawara-shi
International Classification:
G06T 11/00
G06T 7/00
G06N 3/08
Abstract:
Data acquired from a scan of an object can be decomposed into frequency components. The frequency components can be input into a trained model to obtain processed frequency components. These processed frequency components can be composed and used to generate a final image. The trained model can be trained, independently or dependently, using frequency components covering the same frequencies as the to-be-processed frequency components. In addition, organ specific processing can be enabled by training the trained model using image and/or projection datasets of the specific organ.

Apparatus And Method That Uses Deep Learning To Correct Computed Tomography (Ct) With Sinogram Completion Of Projection Data

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US Patent:
20210290193, Sep 23, 2021
Filed:
Jun 4, 2021
Appl. No.:
17/339093
Inventors:
- Otawara-shi, JP
Ruoqiao ZHANG - Arlington Heights IL, US
Zhou YU - Wilmette IL, US
Yan LIU - Vernon Hills IL, US
Assignee:
CANON MEDICAL SYSTEMS CORPORATION - Otawara-shi
International Classification:
A61B 6/00
G06T 11/00
G06T 5/20
G06T 5/10
G06N 3/08
G06N 3/04
G06T 5/50
Abstract:
A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.

Apparatus And Method That Uses Deep Learning To Correct Computed Tomography (Ct) With Sinogram Completion Of Projection Data

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US Patent:
20200196972, Jun 25, 2020
Filed:
Dec 20, 2018
Appl. No.:
16/227251
Inventors:
- Otawara-shi, JP
Ruoqiao Zhang - Arlington Heights IL, US
Zhou Yu - Wilmette IL, US
Yan Liu - Vernon Hills IL, US
Assignee:
Canon Medical Systems Corporation - Otawara-shi
International Classification:
A61B 6/00
G06T 11/00
G06T 5/20
G06T 5/10
G06N 3/04
G06N 3/08
Abstract:
A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.

Systems And Methods For Guided De-Noising For Computed Tomography

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US Patent:
20170010224, Jan 12, 2017
Filed:
Sep 20, 2016
Appl. No.:
15/270368
Inventors:
- Schenectady NY, US
- Notre Dame IN, US
- West Lafayette IN, US
Ken David Sauer - South Bend IN, US
Charles Bouman - Lafayette IN, US
Ruoqiao Zhang - Lafayette IN, US
International Classification:
G01N 23/04
G06T 7/00
G01T 1/36
G06T 5/10
G06T 11/00
G01N 23/087
G06T 3/00
G06T 5/00
Abstract:
A method includes obtaining spectral computed tomography (CT) information via an acquisition unit having an X-ray source and a CT detector. The method also includes, generating, with one or more processing units, using at least one image transform, a first basis image and a second basis image using the spectral CT information. Further, the method includes performing, with the one or more processing units, guided processing on the second basis image using the first basis image as a guide to provide a modified second basis image. Also, the method includes performing at least one inverse image transform using the first basis image and the modified second basis image to generate at least one modified image.

Systems And Methods For Guided De-Noising For Computed Tomography

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US Patent:
20160171648, Jun 16, 2016
Filed:
Dec 11, 2014
Appl. No.:
14/566874
Inventors:
- Schenectady NY, US
Debashish Pal - Waukesha WI, US
Jie Tang - Waukesha WI, US
Ken David Sauer - South Bend IN, US
Charles Bouman - Lafayette IN, US
Ruoqiao Zhang - Lafayette IN, US
International Classification:
G06T 3/00
G06T 5/10
G01N 23/087
G01N 23/04
G01T 1/36
Abstract:
A method includes obtaining spectral computed tomography (CT) information via an acquisition unit having an X-ray source and a CT detector. The method also includes, generating, with one or more processing units, using at least one image transform, a first basis image and a second basis image using the spectral CT information. Further, the method includes performing, with the one or more processing units, guided processing on the second basis image using the first basis image as a guide to provide a modified second basis image. Also, the method includes performing at least one inverse image transform using the first basis image and the modified second basis image to generate at least one modified image.

Methods And Systems For Performing Model-Based Image Processing

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US Patent:
20150146845, May 28, 2015
Filed:
Nov 27, 2013
Appl. No.:
14/092383
Inventors:
- Schenectady NY, US
Ruoqiao Zhang - W. Lafayette IN, US
Charles Bouman - W. Lafayette IN, US
Ken Sauer - Notre Dame IN, US
Assignee:
General Electric Company - Schenectady NY
International Classification:
G06T 11/00
A61B 6/00
US Classification:
378 19, 382131
Abstract:
Methods and systems for model-based image processing are provided. One method includes selecting at least one reference image from a plurality of reference images, partitioning the at least one reference image into a plurality of patches, generating a probability distribution for each of the patches, and generating a model of a probability distribution for the at least one reference image using the probability distributions for each of the patches.
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Ruoqiao Zhang from Vernon Hills, IL, age ~37 Get Report