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Ajay Gopinath

from Bedford, MA
Age ~43
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Also known as:
Ajay Giopinath
Phone and address:
73 Kendall Ct, Bedford, MA 01730
Connected to:
Ajit Gopinath, 49Aravind Gopinath, 35Ashok Gopinath, 60Ashoka Gopinath, 45Dhruv Gopinath, 35Dinesh A Gopinath, 56Harikrishnan Gopinathan, 47Kavitha R Gopinath, 37Kiruthiga Gopinath, 42Nithin Gopinath, 47

Ajay Gopinath Phones & Addresses

  • 73 Kendall Ct, Bedford, MA 01730
  • Falmouth, MA
  • Waltham, MA
  • Austin, TX
  • Potsdam, NY
  • Charlottesville, VA
  • Houston, TX

Work

Company: University of texas at austin Aug 2007 Position: Research assistant

Education

School / High School: The University of Texas at Austin Jan 2007 Specialities: PhD in Electrical and Computer Engineering

Skills

Image Processing • Computer Vision • Tomographic Reconstruction • Segmentation • Registration • Inverse Problems • Optimization Methods • Biomedical Image Analysis.

Resumes

Resumes

Ajay Gopinath Photo 1

Director, Advanced Technology

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Location:
12 Oak Park Dr, Bedford, MA 01730
Industry:
Research
Work:
Abbott
Director, Advanced Technology
Abbott May 2017 - Jun 2018
Senior Principal Engineer, R and D
Ninepoint Medical, Inc. Feb 2016 - Apr 2017
Principal Imaging Scientist
St. Jude Medical 2015 - Feb 2016
Principal R and D Engineer, Advanced Technology
St. Jude Medical Mar 2012 - 2015
Senior R and D Engineer, Advanced Technology
Education:
The University of Texas at Austin 2007 - 2012
Doctorates, Doctor of Philosophy, Computer Engineering Clarkson University 2003 - 2005
Master of Science, Masters, Electrical Engineering Visvesvaraya Technological University 1998 - 2002
Bachelor of Engineering, Bachelors, Communication, Electronics
Skills:
Image Processing
Optimization
Registration
Inverse Problems
Segmentation
Medical Imaging
Image Analysis
Languages:
English
Kannada
Hindi
Ajay Gopinath Photo 2

Ajay Gopinath

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Ajay Gopinath Photo 3

Ajay Gopinath Austin, TX

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Work:
University of Texas at Austin

Aug 2007 to 2000
Research Assistant
GE Global Research

2005 to 2007
Scientist
Education:
The University of Texas at Austin
Jan 2007 to Jan 2011
PhD in Electrical and Computer Engineering
Clarkson University
Jan 2003 to Jan 2005
MS in Electrical Engineering
Visvesvaraya Technological University
Skills:
Image Processing, Computer Vision, Tomographic Reconstruction, Segmentation, Registration, Inverse Problems, Optimization Methods, Biomedical Image Analysis.

Publications

Us Patents

Systems And Methods Of Identifying Vessel Attributes Using Extravascular Images

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US Patent:
20230054891, Feb 23, 2023
Filed:
Aug 18, 2022
Appl. No.:
17/890672
Inventors:
- Westford MA, US
Shimin Li - Acton MA, US
Ajay Gopinath - Bedford MA, US
Jacob Segev - Haifa, IL
Lorina Dascal - Haifa, IL
Assignee:
LightLab Imaging, Inc. - Westford MA
International Classification:
A61B 5/02
G16H 30/20
Abstract:
Systems and methods are disclosed for identifying features of a blood vessel using extravascular and intravascular images in order to estimate a virtual flow reserve (VFR) of the imaged blood vessel. Aspects of the disclosure include using extravascular images to estimate the size of the blood vessel in regions that have not been intravascularly imaged. The VFR estimation may be based on a resistance model that incorporates both the intravascular image data and the estimated blood vessel size. In other aspects, multiangled extravascular images are captured and analyzed in order to identify the size and orientation of branch vessels.

Deep Learning Based Approach For Oct Image Quality Assurance

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US Patent:
20230018499, Jan 19, 2023
Filed:
Jul 12, 2022
Appl. No.:
17/862991
Inventors:
- Westford MA, US
Ajay Gopinath - Bedford MA, US
Humphrey Chen - Acton MA, US
Kyle Edward Savidge - Medford MA, US
Angela Zhang - Stow MA, US
Gregory Patrick Amis - Westford MA, US
Assignee:
LightLab Imaging, Inc. - Westford MA
International Classification:
G06T 7/00
G16H 50/20
G16H 30/40
Abstract:
Aspects of the disclosure relate to systems, methods, and algorithms to train a machine learning model or neural network to classify OCT images. The neural network or machine learning model can receive annotated OCT images indicating which portions of the OCT image are blocked and which are clear as well as a classification of the OCT image as clear or blocked. After training, the neural network can be used to classify one or more new OCT images. A user interface can be provided to output the results of the classification and summarize the analysis of the one or more OCT images.

Fibrotic Cap Detection In Medical Images

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US Patent:
20230005139, Jan 5, 2023
Filed:
Jun 30, 2022
Appl. No.:
17/854994
Inventors:
- Westford MA, US
Ajay Gopinath - Bedford MA, US
Gregory Patrick Amis - Westford MA, US
Kyle Savidge - Medford MA, US
Assignee:
LightLab Imaging, Inc. - Westford MA
International Classification:
G06T 7/00
G06T 7/12
G16H 30/40
G16H 50/20
G06N 20/00
Abstract:
Aspects of the disclosure provide for methods, systems, and apparatuses, including computer-readable storage media, for lipid detection by identifying fibrotic caps in medical images of blood vessels. A method includes receiving one or more input images of a blood vessel and processing the one or more input images using a machine learning model trained to identify locations of fibrotic caps in blood vessels. The machine learning model is trained using a plurality of training images each annotated with locations of one or more fibrotic caps. A method includes identifying and characterizing fibrotic caps of lipid pools based on differences in radial signal intensities measured at different locations of an input image. A system can generate one or more output images having segments that are visually annotated representing predicted locations of fibrotic caps covering lipidic plaques.

Systems And Methods Of Combined Imaging

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US Patent:
20210085275, Mar 25, 2021
Filed:
Sep 18, 2020
Appl. No.:
17/025473
Inventors:
- Westford MA, US
Ajay Gopinath - Bedford MA, US
Assignee:
LightLab Imaging, Inc. - Westford MA
International Classification:
A61B 6/00
A61B 5/0402
A61B 90/00
Abstract:
Aspects of the disclosure relate to the combination and display of both live and non-live patient images. The features described include collecting angiographic image data, and correlating angiographic image frames to time-varying data relating to the patient's heart cycle. This time-varying data may then be compared with the patient's live heart cycle data so that the collected angiographic image frames can be interlaced within a display of live fluoroscopic images of the patient.

Longitudinal Display Of Coronary Artery Calcium Burden

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US Patent:
20210042927, Feb 11, 2021
Filed:
Aug 5, 2020
Appl. No.:
16/985623
Inventors:
- Westford MA, US
Ajay Gopinath - Bedford MA, US
Mark Hoeveler - Eliot ME, US
Assignee:
LightLab Imaging, Inc. - Westford MA
International Classification:
G06T 7/00
G06T 11/00
G06T 7/38
G16H 50/30
G16H 30/40
G16H 50/50
A61B 5/02
A61B 5/00
Abstract:
The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

Systems And Methods For Classification Of Arterial Image Regions And Features Thereof

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US Patent:
20200226422, Jul 16, 2020
Filed:
Jan 13, 2020
Appl. No.:
16/741718
Inventors:
- Westford MA, US
Ajay Gopinath - Bedford MA, US
Kyle Savidge - Medford MA, US
Assignee:
LightLab Imaging, Inc. - Westford MA
International Classification:
G06K 9/62
G06N 3/08
G06N 3/04
Abstract:
In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

Method, Apparatus, And System To Identify Branches Of A Blood Vessel

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US Patent:
20200167923, May 28, 2020
Filed:
Jan 31, 2020
Appl. No.:
16/778252
Inventors:
- Westford MA, US
Ajay Gopinath - Bedford MA, US
Assignee:
LightLab Imaging, Inc. - Westford MA
International Classification:
G06T 7/00
G06T 5/00
A61B 5/00
A61B 5/02
G06T 7/13
G06T 7/11
Abstract:
In part, the disclosure relates to an automated method of branch detection with regard to a blood vessel imaged using an intravascular modality such as OCT, IVUS, or other imaging modalities. In one embodiment, a representation of A-lines and frames generated using an intravascular imaging system is used to identify candidate branches of a blood vessel. One or more operators such as filters can be applied to remove false positives associated with other detections.

Stent Planning Systems And Methods Using Vessel Representation

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US Patent:
20180085170, Mar 29, 2018
Filed:
Sep 28, 2017
Appl. No.:
15/718835
Inventors:
- Westford MA, US
Ajay Gopinath - Bedford MA, US
Assignee:
LightLab Imaging, Inc. - Westford MA
International Classification:
A61B 34/10
Abstract:
In part, the disclosure relates to determining a stent deployment location and other parameters using blood vessel data. Stent deployment can be planned such that the amount of blood flow restored from stenting relative to an unstented vessel increases one or more metrics. An end user can specify one or more stent lengths, including a range of stent lengths. In turn, diagnostic tools can generate candidate virtual stents having lengths within the specified range suitable for placement relative to a vessel representation. Blood vessel distance values such as blood vessel diameter, radius, area values, chord values, or other cross-sectional, etc. its length are used to identify stent landing zones. These tools can use or supplement angiography data and/or be co-registered therewith. Optical imaging, ultrasound, angiography or other imaging modalities are used to generate the blood vessel data.
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Ajay Gopinath from Bedford, MA, age ~43 Get Report