Abstract: A method and apparatus for inspecting a test object with a non-planar feature. A pattern of light is transmitted from a first array of optical fibers associated with a sensor structure onto a surface of the test object at a location of the non-planar feature. The pattern of light is configured to cause sound waves in the test object when the pattern of light encounters the test object. A response to the sound waves is detected using a second array of optical fibers associated with the sensor structure. A determination is made as to whether an inconsistency is present in the test object at the location of the non-planar feature from the response to the sound waves detected using the second array of optical fibers.

Abstract: A method and apparatus for identifying deformation of a structure. Training deformation data is identified for each training case in a plurality of training cases. Training strain data is identified for each training case in the plurality of training cases. The training deformation data and the training strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model. A group of parameters for the heuristic model is adjusted using the training deformation data and the training strain data for the each training case in the plurality of training cases such that the heuristic model is trained to generate estimated deformation data for the structure based on input strain data. The estimated deformation data has a desired level of accuracy.

Abstract: A method and apparatus for inspecting a test object. The apparatus comprises an inspection vehicle, a sensor structure, a first array of optical fibers, and a second array of optical fibers. The inspection vehicle is configured to move on a surface of the test object. The sensor structure is associated with the inspection vehicle. The first array of optical fibers is associated with the sensor structure. The first array of optical fibers is configured to transmit a pattern of light towards the surface of the test object and the pattern of light is configured to cause sound waves in the test object when the pattern of light encounters the test object. The second array of optical fibers is associated with the sensor structure. The second array of optical fibers is configured to detect a response to the sound waves.

Abstract: A method and apparatus for identifying deformation of a structure. Training deformation data is identified for each training case in a plurality of training cases. Training strain data is identified for each training case in the plurality of training cases. The training deformation data and the training strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model. A group of parameters for the heuristic model is adjusted using the training deformation data and the training strain data for the each training case in the plurality of training cases such that the heuristic model is trained to generate estimated deformation data for the structure based on input strain data. The estimated deformation data has a desired level of accuracy.

Abstract: A method for and apparatus for inspecting a location on a test object with a number of obstructions to reaching the location. An elongate optical fiber carrier holding a number of optical fibers is moved to the location on the test object with the number of obstructions to reaching the location. A pattern of light is transmitted from the number of optical fibers onto a surface of the test object at the location. The pattern of the light is configured to cause sound waves in the test object when the pattern of the light encounters the surface of the test object. A response is detected to the sound waves using the number of optical fibers.

Abstract: A method and apparatus comprises a sensor structure, a first array of optical fibers, and a second array of optical fibers. The first array of optical fibers is associated with the sensor structure. The first array of optical fibers is configured to transmit a pattern of light towards a test object and the pattern of light is configured to cause sound waves in the test object when the pattern of light encounters the test object. The second array of optical fibers is associated with the sensor structure. The second array of optical fibers is configured to detect a response to the sound waves.

Abstract: A method and apparatus for identifying deformation of a structure. Training deformation data is identified for each training case in a plurality of training cases. Training strain data is identified for each training case in the plurality of training cases. The training deformation data and the training strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model. A group of parameters for the heuristic model is adjusted using the training deformation data and the training strain data for the each training case in the plurality of training cases such that the heuristic model is trained to generate estimated deformation data for the structure based on input strain data. The estimated deformation data has a desired level of accuracy.

Abstract: A method and apparatus for generating a tension wave. A beam of coherent light is directed to an absorber surface of a transducer structure. A compression wave is generated within the transducer structure. The compression wave is reflected on a reflecting surface of the transducer structure to form the tension wave. The tension wave is directed through a test object in a desired direction using a configuration of the reflecting surface relative to the test object.

Abstract: A method for and apparatus for inspecting a location on a test object with a number of obstructions to reaching the location. An elongate optical fiber carrier holding a number of optical fibers is moved to the location on the test object with the number of obstructions to reaching the location. A pattern of light is transmitted from the number of optical fibers onto a surface of the test object at the location. The pattern of the light is configured to cause sound waves in the test object when the pattern of the light encounters the surface of the test object. A response is detected to the sound waves using the number of optical fibers.

Abstract: A method and apparatus for identifying deformation of a structure. Training deformation data is identified for each training case in a plurality of training cases. Training strain data is identified for each training case in the plurality of training cases. The training deformation data and the training strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model. A group of parameters for the heuristic model is adjusted using the training deformation data and the training strain data for the each training case in the plurality of training cases such that the heuristic model is trained to generate estimated deformation data for the structure based on input strain data. The estimated deformation data has a desired level of accuracy.

Abstract: A method and apparatus for generating a tension wave. A beam of coherent light is directed to an absorber surface of a transducer structure. A compression wave is generated within the transducer structure. The compression wave is reflected on a reflecting surface of the transducer structure to form the tension wave. The tension wave is directed through a test object in a desired direction using a configuration of the reflecting surface relative to the test object.

Abstract: A method and apparatus for multi-energy object inspection using a brilliant x-ray source. A first mono-energetic x-ray image of an object at a first selected energy is generated. A second mono-energetic x-ray image of the object at a second selected energy is generated. The first selected energy is different than the second selected energy. Additional mono-energetic x-ray images may be generated at energies different than previous energies up to n selected energies. The mono-energetic x-ray images are mathematically combined and processed to form a result. The result of processing the mono-energetic x-ray images is presented. The result comprises processed mono-energetic x-ray image data describing materials in the object with greater sensitivity, identifying the layers, and identifying the material composition than in the first image or the second image.

Abstract: A method and apparatus for multi-energy object inspection using a brilliant x-ray source. A first mono-energetic x-ray image of an object at a first selected energy is generated. A second mono-energetic x-ray image of the object at a second selected energy is generated. The first selected energy is different than the second selected energy. Additional mono-energetic x-ray images may be generated at energies different than previous energies up to n selected energies. The mono-energetic x-ray images are mathematically combined and processed to form a result. The result of processing the mono-energetic x-ray images is presented. The result comprises processed mono-energetic x-ray image data describing materials in the object with greater sensitivity, identifying the layers, and identifying the material composition than in the first image or the second image.

Abstract: A system is provided for radiographic inspection of an object comprising multiple having different material properties. The system comprises a radiation source configured to generate radiation, a display unit for generating a graphical user interface (GUI) including multiple fields. A user enters input data via the fields in the GUI. The input data relates to one or more material properties for each of the regions. A processor is configured to compute a plurality of exposure parameters based on the input data.

Abstract: A system is provided for radiographic inspection of an object comprising multiple having different material properties. The system comprises a radiation source configured to generate radiation, a display unit for generating a graphical user interface (GUI) including multiple fields. A user enters input data via the fields in the GUI. The input data relates to one or more material properties for each of the regions. A processor is configured to compute a plurality of exposure parameters based on the input data.

Abstract: The present invention provides a method to detect tube spits and to reduce spit related artifacts in a computed tomography imaging system. A way of detecting tube spit is to monitor the generator kilovolt or milliamp waveforms. Changes in kV waveform follow closely with those in offset-corrected projection data. If a tube-spit event is not detected, processing proceeds without tube spit correction. If a tube-spit event is detected, a tube spit correction is performed. The objective of tube-spit correction is to remove image artifacts due to the occurrence of a tube-spit event.

Abstract: An imaging tube coolant volume control system (14) for an imaging tube (18) includes a compensation tank (80) that is configured to fluidically couple an imaging tube cooling circuit (11). The compensation tank (80) includes a cooling fluid (17) and a compensation-dividing member (100). The member (100) is adjustable in response to the change in volume of the cooling fluid (17). An overflow vessel (82) is fluidically coupled to the compensation tank (80). A compensation valve (86) is coupled between the compensation tank (80) and the overflow vessel (82) and allows flow of the cooling fluid between the compensation tank (80) and the overflow vessel (82) when pressure of the cooling fluid (17) is greater than or equal to a first predetermined pressure level.

Abstract: In one aspect, the present invention relates to tube-spit detection and correction. In an exemplary embodiment, and once a sample of projection data has been collected, the projection data is preprocessed in accordance with known preprocessing algorithms including applying a detector primary speed correction to the projection data. Such primary speed correction removes contamination of previous signal sample from the current sample. After primary speed correction, tube-spit detection is performed on the sample. Such detection can be performed using many different methods. Generally, the objective is to determine whether the x-ray source experienced a drop in power. If a tube-spit event is not detected, then processing proceeds with further preprocessing and image reconstruction. If a tube-spit event is detected, then a tube spit correction is performed. Generally, the objective of tube-spit correction is to remove image artifacts due to the occurrence of a tube-spit event.

Abstract: A neural network prediction has been provided for predicting radiation exposure and/or Air-Kerma at a predefined arbitrary distance during an x-ray exposure; and for predicting radiation exposure and/or Air-Kerma area product for a radiographic x-ray exposure. The Air-Kerma levels are predicted directly from the x-ray exposure parameters. The method or model is provided to predict the radiation exposure or Air-Kerma for an arbitrary radiographic x-ray exposure by providing input variables to identify the spectral characteristics of the x-ray beam, providing a neural net which has been trained to calculate the exposure or Air-Kerma value, and by scaling the neural net output by the calibrated tube efficiency, and the actual current through the x-ray tube and the duration of the exposure. The prediction for exposure/Air-Kerma further applies the actual source-to-object distance, and the prediction for exposure/Air-Kerma area product further applies the actual imaged field area at a source-to-image distance.

Abstract: Time consuming calibration of a multi-element x-ray detector for an x-ray computed tomography machine that has multi-sample rate capabilities is reduced by determining through the use of air-scans, a scalar relationship between sensitivity of detector elements as a function of sampling rate. This scalar relationship is in vector form and may be applied to independently obtain calibration vectors at a base scan rate to provide effective calibration vectors at a variety of scan rates without the need for time consuming daily calibration scans at each of those sample rates.