Abstract: An image calibration method includes capturing and correcting a flood field image for background signal and effects of known image-panel features (dead/bad pixels). The corrected image is processed to separate frequencies characteristic of relative pixel sensitivities from frequencies characteristic of radiation energy fluence. The incident energy fluence has a known maximum in-field energy fluence gradient. A model that describes the incident energy fluence on a detector is generated or received. The corrected image may be modeled at frequencies at or below the maximum in-field energy fluence gradient. A pixel sensitivity matrix (PSM) is generated by adjusting the corrected image with the model of the incident energy fluence on the detector. For example, the corrected image signal may be divided by the model or the model may be subtracted from the corrected image. The PSM may be used to correct additional raw images captured by the detector.

Abstract: An image calibration method includes capturing and correcting a flood field image for background signal and effects of known image-panel features (dead/bad pixels). The corrected image is processed to separate frequencies characteristic of relative pixel sensitivities from frequencies characteristic of radiation energy fluence. The incident energy fluence has a known maximum in-field energy fluence gradient. A model that describes the incident energy fluence on a detector is generated or received. The corrected image may be modeled at frequencies at or below the maximum in-field energy fluence gradient. A pixel sensitivity matrix (PSM) is generated by adjusting the corrected image with the model of the incident energy fluence on the detector. For example, the corrected image signal may be divided by the model or the model may be subtracted from the corrected image. The PSM may be used to correct additional raw images captured by the detector.

Abstract: An image calibration method includes capturing and correcting a flood field image for background signal and effects of known image-panel features (dead/bad pixels). The corrected image is processed to separate frequencies characteristic of relative pixel sensitivities from frequencies characteristic of radiation energy fluence. The incident energy fluence has a known maximum in-field energy fluence gradient. A model that describes the incident energy fluence on a detector is generated or received. The corrected image may be modeled at frequencies at or below the maximum in-field energy fluence gradient. A pixel sensitivity matrix (PSM) is generated by adjusting the corrected image with the model of the incident energy fluence on the detector. For example, the corrected image signal may be divided by the model or the model may be subtracted from the corrected image. The PSM may be used to correct additional raw images captured by the detector.

Abstract: An image calibration method includes capturing and correcting a flood field image for background signal and effects of known image-panel features (dead/bad pixels). The corrected image is processed to separate frequencies characteristic of relative pixel sensitivities from frequencies characteristic of radiation energy fluence. The incident energy fluence has a known maximum in-field energy fluence gradient. A model that describes the incident energy fluence on a detector is generated or received. The corrected image may be modeled at frequencies at or below the maximum in-field energy fluence gradient. A pixel sensitivity matrix (PSM) is generated by adjusting the corrected image with the model of the incident energy fluence on the detector. For example, the corrected image signal may be divided by the model or the model may be subtracted from the corrected image. The PSM may be used to correct additional raw images captured by the detector.

Abstract: An image calibration method includes capturing and correcting a flood field image for background signal and effects of known image-panel features (dead/bad pixels). The corrected image is processed to separate frequencies characteristic of relative pixel sensitivities from frequencies characteristic of radiation energy fluence. The incident energy fluence has a known maximum in-field energy fluence gradient. A model that describes the incident energy fluence on a detector is generated or received. The corrected image may be modeled at frequencies at or below the maximum in-field energy fluence gradient. A pixel sensitivity matrix (PSM) is generated by adjusting the corrected image with the model of the incident energy fluence on the detector. For example, the corrected image signal may be divided by the model or the model may be subtracted from the corrected image. The PSM may be used to correct additional raw images captured by the detector.

Abstract: A system (106) visualizing an image registration mapping in an intuitive interactive manner. The system (106) includes a display (110) and one or more processors (116). The processors (116) are programmed to receive a first image and a second image and obtain an image registration mapping from the first image to the second image. Even more, the processors (116) are programmed to display the first image adjacent to the second image on the display (110) and obtain one or more reference image locations. Each of the reference image locations is defined in the coordinate frame of one of the first image and the second image. Moreover, the processors (116) are programmed to highlight each of the reference image locations on the one of the first image and the second image and highlight a correlated image location for each of the reference image locations in the other one of the first image and the second image. The correlated image locations are determined using the image registration mapping.

Abstract: A system (106) visualizing an image registration mapping in an intuitive interactive manner. The system (106) includes a display (110) and one or more processors (116). The processors (116) are programmed to receive a first image and a second image and obtain an image registration mapping from the first image to the second image. Even more, the processors (116) are programmed to display the first image adjacent to the second image on the display (110) and obtain one or more reference image locations. Each of the reference image locations is defined in the coordinate frame of one of the first image and the second image. Moreover, the processors (116) are programmed to highlight each of the reference image locations on the one of the first image and the second image and highlight a correlated image location for each of the reference image locations in the other one of the first image and the second image. The correlated image locations are determined using the image registration mapping.