Ge Sensing & Inspection Technologies, Gmbh

General Electric Company

Baker Hughes Holdings LLC

Ge Inspection Technologies, Gmbh

Alexander Suppes

Nils Rothe

Eberhard Neuser

Anja Frost

Michael Hoetter

Alex Sawatzky

Pavel Pokutnev

Sheri George

Haribaskar Govindasamy

Andreas Fischer

Eugen Trapet

Utkarsh Madhav Kulkarni

Marko Rosenmueller

Michael Hötter

Dang Hai Tran Vu

Pavel Blazek

A computed tomography (CT) system including a scanning unit having an x-ray emitter, a detector arranged to receive x-rays emitted from the x-ray emitter during a scanning operation, and a manipulating unit arranged to rotate an object about a rotational axis and move the object through a plurality of positions along a path, defined by the rotational axis, in between the x-ray emitter and the detector. The CT system can also include a computing device, including a memory storing computer-executable instructions and at least one data processor. The processor is arranged to execute the instructions, which cause the processor to perform operations including: receiving, from the scanning unit, first data characterizing the path, and receiving, from the scanning unit, second data characterizing the one or more x-ray images of the object at at least one position of the plurality of positions, determining a compensation to be applied to the second data based on the first data, reconstructing an image of the object based on the first data, the second data, and the compensation, and providing the reconstructed image.

Systems and methods for computed tomography inaccuracy compensation

A method for detecting structures is provided. The method can include receiving inspection image data characterizing a region of interest of an object being inspected. The regions of interest can include one or more structures of the object. The method can also include determining, using a computer vision algorithm, a structure within the region of interest with respect to photometric properties of pixel data in the inspection image data. The structure can be determined using a predictive model trained to determine image filter parameter values for image filters of the computer vision algorithm based on applying optimization techniques using training image data and annotation data. An indication of the structure can be provided, for example for display or storage in memory. Systems and computer-readable mediums implementing the method are also provided.

Detection of structures

A system and method for calibrating a computed tomography (CT) scanner including scanning a calibration apparatus with the CT scanner, and determining a first scan edge of a first calibration layer, a second scan edge of a second calibration layer, and a floating point of an opening from the scan. The method also includes determining a first scan dimension and second scan dimension measured in the longitudinal direction from the first scan edge to the floating point, and the second scan edge to the floating point, respectively. The method also includes determining a first scan overhang based on a difference between the first scan dimension and the second scan dimension and comparing the first scan overhang to the calibration overhang. The method also includes determining a first level of uncertainty for the CT scanner based on the comparing.

Apparatus and method for computed tomography system calibration

Systems and methods for non-destructive testing by computed tomography are provided. The system can include a stationary radiation source, a stage, and a plurality of stationary radiation detectors. The source can be configured to emit, from a focal point, a beam of penetrating radiation having a three-dimensional geometry and to direct the beam in a path incident upon a target. The stationary radiation source can be positioned with respect to the plurality of stationary radiation detectors and the stage such that, a first plurality of beam segment paths is defined between the focal point and respective sensing faces of the plurality of radiation detectors and at least one second beam segment path is defined between the focal point and a predetermined gap.

Fast industrial, computed tomography for large objects

Scatter correction for tomography: for each position, two images are acquired, a first image without and a second image with a scatter reducing aperture plate (). A scatter image is calculated by subtracting the second image from the first image. The apertures () in the scatter reducing plate () are arranged hexagonally in order to optimise the packaging density of the apertures.

Imaging system and method with scatter correction

Fast industrial computed tomography for large objects

A method for imaging an object is presented. The method includes acquiring a first projection image of the object using a source and a detector, positioning a scatter rejecting aperture plate between the object and the detector, and acquiring a second projection image of the object with the scatter rejecting aperture plate disposed between the object and the detector. A scatter image of the object is generated based on the first projection image and the second projection image, and stored for subsequent imaging. A volumetric CT system for imaging an object is also presented. The system is configured to acquire a plurality of projection images of the object from a plurality of plurality of projection angles.

Method and apparatus are provided for calibration or verification of accuracy specification of a computed tomographic system. In one embodiment, the apparatus can include a base structure, a first set of test objects arranged along a first axis and coupled to the base structure, and a second set of test objects arranged along a second axis and coupled to the base structure. The first set of test objects and the second set of test objects have a first geometry. The apparatus can also include a third set of test objects and a fourth set of test objects. The third set of test objects, and the fourth set of test objects have a second geometry different from the first geometry. Locations of the first, second third and fourth set of test objects are spatially fixed with respect to the base structure. The apparatus is a test specimen adapted for calibration or accuracy verification of computed tomography system.

Computed tomographic system calibration

A method of using computed tomography for determining a volumetric representation of a sample, involving a first reconstruction for reconstructing first reconstructed volume data of the sample from first x-ray projection data of the sample taken by an x-ray system, a second reconstruction for reconstructing second reconstructed volume data of the sample from second x-ray projection data of the sample taken by an x-ray system, characterized by calculating first individual confidence measures for single voxels of the first reconstructed volume data, calculating second individual confidence measures for single voxels of the second reconstructed volume data, and calculating, in a subsequent step, at least one resulting set of individual values for each voxel based on the first individual confidence measures and the second individual confidence measures.

Computed tomography method, computer software, computing device and computed tomography system for determining a volumetric representation of a sample

A method and system for determining geometric imaging properties of a flat panel detector in an x-ray inspection system are described herein. The method can include arranging a calibration phantom between an x-ray source and the flat panel detector, the calibration phantom including at least one discrete geometric object. Additionally, the method can include recording at least one x-ray image of the calibration phantom with the flat panel detector. At least one discrete geometric shape is generated in the x-ray image by imaging the at least one discrete geometric object of the calibration phantom. Further, the method can include determining a location-dependent distortion error of the flat panel detector from the at least one x-ray image on the basis of at least one characteristic of the at least one discrete geometric shape. All characteristics of the at least one discrete geometric shape used for determining the location-dependent distortion error are independent of the dimensions of the calibration phantom.

Method for determining geometric imaging properties of a flat panel detector, correspondingly adapted X-ray inspection system and calibration phantom

Aspects of the invention provide a solution for analyzing an object, such as a part of a turbo machine. A planar surface is generated using a curved reformat function based on a surface of a three-dimensional (3D) image of an object. A peel of the 3D image that is adjacent to the surface is determined. Based on the peel, a second planar surface is generated. These two, and/or other similarly generated planar surfaces can be analyzed to determine characteristics of the original object.

Method system and computer product for non-destructive object analysis

A computed tomography method for determining a volumetric representation of a sample comprising reconstruction initial volume data of the sample from x-ray projections of the sample taken by an x-ray system, determining a part of the reconstructed initial volume data to be updated, and executing an iterative update process configured to generate, using an iterative reconstruction method, updated volume data only for the part of the volume data determined to be updated. Determining the part of the sample volume to be updated comprises individually evaluating every single voxel in the reconstructed initial volume data, based on available quality information for the reconstructed initial volume data, whether or not the voxel fulfils a predetermined condition indicating that an update is required for the voxel, and the iterative update process generates the updated volume data only for the voxels which have been determined that an update is required.

Computed tomography method, and system

A method for determining confidence values for volume units includes acquiring real projections of an object in a tomography system, reconstructing an image of the object from the real projections, generating artificial projections, for each volume unit comparing the real projections with the artificial projections and generating a confidence measure for each of the volume units.

System and method for determining confidence measurements of single volume elements in computer tomography

A computed tomography method for determining a volumetric representation of a sample comprises using reconstructed volume data of the sample from x-ray projections of the sample taken by an x-ray system, computing a set of artificial projections of said sample by a forward projection from said reconstructed volume data, and determining, essentially from process data of said reconstruction including said reconstructed volume data and/or said x-ray projections, individual confidence measures for single voxels of said volume data based on calculating, for each of said measured x-ray projections, the difference between the contribution of this measured x-ray projection to the voxel under inspection and the contribution from a corresponding artificial projection to the voxel under inspection.

Embodiments of a system and method are described that automate processes to reconstruct models from two-dimensional images. In one embodiment, the systems comprise an apparatus that can acquire a plurality of images of an object and automatedly form a boundary about the object in each of the images. The boundary highlights a portion of the image that will form the basis for the model. The apparatus can also select a primary image from the images, wherein the boundary in the primary image is narrowest relative to an axis that is common to each of the images. In one example, the apparatus can select a secondary image, which is perpendicular to the primary image, and reconstruct the primary image and the second image to generate the model.

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Wunstorf, Germany

Alexander Suppes