Neural network-based corrector for photon counting detectors
AssigneeRensselaer Polytechnic Institute
InventorsGe Wang, Ruibin Feng, David Rundle
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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.
Claims38
A method for x-ray photon count data correction, the method comprising:
- receiving, by an artificial neural network (ANN), a detected photon count from a photon counting detector, corresponding to an attenuated energy spectrum, which is related to characteristics of an x-ray source used for imaging and an object being imaged;
- and
- correcting, by the ANN, after training, the detected photon count to produce a corrected photon count in an energy window,
- wherein the training comprises training the ANN using parameters including a trigger threshold, and
- wherein the trigger threshold is greater than or equal to a maximum energy of an x-ray energy spectrum output fromm the x-ray source.
A method for photon count correction, the method comprising:
- receiving, by a trained artificial neural network (ANN), a detected photon count from a photon counting detector, the detected photon count corresponding to an attenuated energy spectrum, the attenuated energy spectrum being related to characteristics of an imaging object and based on an incident energy spectrum;
- and
- correcting, by the trained ANN, the detected photon count to produce a corrected photon count in an energy window,
- wherein the training comprises training the ANN using parameters including a trigger threshold, and
- wherein the trigger threshold is greater than or equal to a maximum energy of an x-ray energy spectrum output from an x-ray source.
A photon count correction system comprising:
- a trained artificial neural network (ANN) configured to
- receive a detected photon count from a photon counting detector, and
- correct the detected photon count to produce a corrected photon count in an energy window,
- wherein the detected photon count corresponds to an attenuated energy spectrum,
- wherein the attenuated energy spectrum is related to characteristics of an imaging object and is based on an incident energy spectrum,
- wherein the ANN is trained using parameters including a trigger threshold, and
- wherein the trigger threshold is greater than or equal to a maximum energy of an x-ray energy spectrum output from an x-ray source.
Dependencies are inferred from the claim text.