Kromek - Greater Pittsburgh Area since May 2013
Sales and Customer Support Engineer
eV Products a division of Endicott Interconnect Technologies - Saxonburg PA Jan 2002 - Apr 2012
Senior Applications Development Engineer / Technical Product Manager / Sales Engineer
Diebold MedSelect Systems Sep 1997 - Jul 2001
Sr Product Manager
Gambro Healthcare Jan 1991 - Sep 1997
Product Manager / Business Systems Specialist
Skills:
R&D Product Development Product Management Dual Energy X-ray Absorptiometry (DEXA) Industrial X-ray Inspection X-ray Food Inspection Gamma Spectroscopy Applications Development Radiation Detectors CdZnTe Detector Fabrication High Speed Photon Counting X-ray Software Development Quality Management Cross-functional Team Leadership Physics Systems Engineering Embedded Software System Design Medical Devices
Viatcheslav Vydrin - Pittsburgh PA, US Robert K. Davis - Pittsburgh PA, US David S. Rundle - Butler PA, US
Assignee:
II-VI INCORPORATED - Saxonburg PA
International Classification:
G01T 1/24
US Classification:
25037009
Abstract:
A radiation detection system is operative for converting () a radiation event into an electrical signal having an amplitude related to the energy of said radiation event, converting () at least a portion of the electrical signal into a count value related to the amplitude of the electrical signal and determining () the energy of the radiation event from the count value.
A specimen inspection system includes a photon source for outputting photons along a transmission path and a conveyor for translating a specimen completely through the transmission path. A radiation detector is positioned offset with respect to the transmission path for detecting photons that are scattered from the transmission path in response to interaction with the specimen passing therethrough. A controller determines from the detected scattered photons that a first material is present in the specimen.
Count Correction In A Photon Counting Imaging System
In a method of count correction for pixels of a pixilated photon counting detector, the average count value output by each of a plurality of pixels during a period of time is determined. A product is determined of the actual average count value and a multiplying correction factor. A corrected count value is then determined for the pixel equal to a sum of the product and an additive correction factor. The multiplying correction factor equals a square root of a quotient of a desired average count value to be output by each of the plurality of pixels during the period of time divided by the actual average count value. The additive correction factor equals a product of the multiplying correction factor and the actual average count value subtracted from the desired average count value.
X-Ray Photon-Counting Data Correction Through Deep Learning
Ge Wang - Loudonville NY, US Mengzhou Li - Troy NY, US David S. Rundle - Butler PA, US
Assignee:
Rensselaer Polytechnic Institute - Troy NY
International Classification:
G01T 1/17 G06N 3/04 G06N 3/08 G01T 1/36
Abstract:
A method for x-ray photon-counting data correction. The method includes generating, by a training data generation module, training input spectral projection data based, at least in part, on a reference spectral projection data. The training input spectral projection data includes at least one of a pulse pileup distortion, a charge splitting distortion, and/or noise. The method further includes training, by a training module, a data correction artificial neural network (ANN) based, at least in part, on training data. The data correction ANN includes a pulse pileup correction ANN, and a charge splitting correction ANN. The training data includes the training input spectral projection data and the reference spectral projection data.
Neural Network-Based Corrector For Photon Counting Detectors
- Troy NY, US Ruibin Feng - Troy NY, US David Rundle - Butler PA, US
Assignee:
Rensselaer Polytechnic Institute - Troy NY
International Classification:
G01T 1/17 G01T 1/18 G06T 7/00
Abstract:
A neural network based corrector for photon counting detectors is described. A method for photon count correction includes receiving, by a trained artificial neural network (ANN), a detected photon count from a photon counting detector. The detected photon count corresponds to an attenuated energy spectrum. The attenuated energy spectrum is related to characteristics of an imaging object and is based, at least in part, on an incident energy spectrum. The method further includes correcting, by the trained ANN, the detected photon count to produce a corrected photon count. The method may include reconstructing, by image reconstruction circuitry, an image based, at least in part, on the corrected photon count.
A Neural Network-Based Corrector For Photon Counting Detectors
- Troy NY, US Ruibin Feng - Troy NY, US David Rundle - Butler PA, US
Assignee:
RENSSELAER POLYTECHNIC INSTITUTE - Troy NY
International Classification:
G01T 1/17 G01T 1/18 G06T 7/00
Abstract:
A neural network based corrector for photon counting detectors is described. A method for photon count correction includes receiving, by a trained artificial neural network (ANN), a detected photon count from a photon counting detector. The detected photon count corresponds to an attenuated energy spectrum. The attenuated energy spectrum is related to characteristics of an imaging object and is based, at least in part, on an incident energy spectrum. The method further includes correcting, by the trained ANN, the detected photon count to produce a corrected photon count. The method may include reconstructing, by image reconstruction circuitry, an image based, at least in part, on the corrected photon count.