Abstract: Techniques and/or systems for generating a two-dimensional projection image of an object under examination from helical data are provided herein. An image plane and a distance, or height, of an examination line lying in a plane parallel to the image plane may be selected with or without user input. Using the selected image plane and examination line, data may be extracted from one or more views indicative of the object. The data that is extracted from the respective views is generally indicative of rays that traverse the examination line and have a trajectory that meets predetermined criteria. Using the extracted data from a plurality of views, one or more projection lines that are substantially parallel to a corresponding image slice are produced and a two-dimensional projection image is generated.

Abstract: The techniques described herein provide for correcting projection data that comprises contamination due to source switching in a multi energy scanner. The correction is a multi-neighbor correction. That is, it uses data from at least two other views of an object (e.g., generally a previous view and a subsequent view) to correct a current view of the object. The multi-neighbor correction may use one or more correction factors to determine how much data from the other two views to use to correct the current view. The correction factor(s) are determined based upon a calibration that utilizes image space data and/or projection space data of a phantom. In this way, the correction factor(s) account for source leakage that occurs in multi energy scanners.

Abstract: Certain imaging systems, such as automatic explosives detection systems, employ techniques that utilize image processing, feature extraction and decision making steps to detect threats in images. Such techniques use segmentation as a first algorithmic step, which extracts data representing objects from image data. Some of the extracted objects are actually composed of multiple distinct physical objects. For these compound objects discrimination becomes difficult because computed object properties are less specific than properties computed for a single physical object. A technique is described which includes splitting such compound objects by separating the data of each component from the rest of the data and using properties of density histograms based on voxel distributions in both density and spatial domains.

Abstract: Techniques and/or systems for generating a two-dimensional projection image of an object under examination from helical data are provided herein. An image plane and a distance, or height, of an examination line lying in a plane parallel to the image plane may be selected with or without user input. Using the selected image plane and examination line, data may be extracted from one or more views indicative of the object. The data that is extracted from the respective views is generally indicative of rays that traverse the examination line and have a trajectory that meets predetermined criteria. Using the extracted data from a plurality of views, one or more projection lines that are substantially parallel to a corresponding image slice are produced and a two-dimensional projection image is generated.

Abstract: A projection image of an object is colored using three-dimensional image data. This may be particularly useful in radiographic imaging applications, for example. In one embodiment, a colored synthetic image is rendered from a colored three-dimensional image of an object, and color components of pixels of the synthetic image are used to determine color components, or color values, for corresponding pixels of a projection image depicting a similar view of the object as the synthetic image. In this way, the two-dimensional projection image is colored similarly to the colored three-dimensional image. For example, the projection image may be colored based upon density (if the three-dimensional image is colored based upon density) so aspects of the object that attenuate a similar amount of radiation but have different densities may be colored differently.

Abstract: Representations of an object in an image generated by an imaging apparatus can comprise two or more separate sub-objects, producing a compound object. Compound objects can negatively affect the quality of object visualization and threat identification performance. As provided herein, a compound object can be separated into sub-objects. Topology score map data, representing topological differences in the potential compound object, may be computed and used in a statistical distribution to identify modes that may be indicative of the sub-objects. The identified modes may be assigned a label and a voxel of the image data indicative of the potential compound object may be relabeled based on the label assigned to a mode that represents data corresponding to properties of a portion of the object that the voxel represents to create image data indicative of one or more sub-objects.

Abstract: The techniques described herein provide for correcting projection data that comprises contamination due to source switching in a multi energy scanner. The correction is a multi-neighbor correction. That is, it uses data from at least two other views of an object (e.g., generally a previous view and a subsequent view) to correct a current view of the object. The multi-neighbor correction may use one or more correction factors to determine how much data from the other two views to use to correct the current view. The correction factor(s) are determined based upon a calibration that utilizes image space data and/or projection space data of a phantom. In this way, the correction factor(s) account for source leakage that occurs in multi energy scanners.

Abstract: A CT scanning system may include a multi-pixel x-ray source, and a detector array. The multi-pixel x-ray source may have a plurality of pixels that are disposed along a z-axis, and that are sequentially activated so as to controllably emit x-rays in response to incident electrons. The detector array may have one or more rows of x-ray detectors that detect the x-rays that are emitted from the pixels and have traversed an object, and generate data for CT image reconstruction system. In third generation CT scanning systems, the number of detector rows may be reduced. Multi-pixel x-ray source implementation of saddle curve geometry may render a single rotation single organ scan feasible. Using a multi-pixel x-ray source in stationary CT scanning systems may allow x-ray beam design with a minimal coverage to satisfy mathematical requirements for reconstruction.

Abstract: The disclosed CT scanner comprises at least one source of X-rays; a detector array comprising a plurality of detectors; and an X-ray filter mask arrangement disposed between the source of X-rays and detector array so as to modify the spectra of the X-rays transmitted from the source through the mask to at least some of the detectors so that the X-ray spectra detected by at least one set of detectors is different from the X-ray spectra detected by at least one other set of detectors.

Abstract: A method of and a system for identifying objects using local distribution features from multi-energy CT images are provided. The multi-energy CT images include a CT image, which approximates density measurements of scanned objects, and a Z image, which approximates effective atomic number measurements of scanned objects. The local distribution features are first and second order statistics of the local distributions of the density and atomic number measurements of different portions of a segmented object. The local distributions are the magnitude images of the first order derivative of the CT image and the Z image. Each segmented object is also divided into different portions to provide geometrical information for discrimination.

Abstract: Certain imaging systems, such as automatic explosives detection systems, employ techniques that utilize image processing, feature extraction and decision making steps to detect threats in images. Such techniques use segmentation as a first algorithmic step, which extracts data representing objects from image data. Some of the extracted objects are actually composed of multiple distinct physical objects. For these compound objects discrimination becomes difficult because computed object properties are less specific than properties computed for a single physical object. A technique is described which includes splitting such compound objects by separating the data of each component from the rest of the data and using properties of density histograms based on voxel distributions in both density and spatial domains.

Abstract: The disclosed CT scanner comprises at least one source of X-rays; a detector array comprising a plurality of detectors; and an X-ray filter mask arrangement disposed between the source of X-rays and detector array so as to modify the spectra of the X-rays transmitted from the source through the mask to at least some of the detectors so that the X-ray spectra detected by at least one set of detectors is different from the X-ray spectra detected by at least one other set of detectors.

Abstract: A method of and a system for variable pitch CT scanning for baggage screening and variable pitch image reconstruction are disclosed.

Abstract: A method of and a system for displaying volumetric multi-energy CT images are disclosed, wherein a CT image, a Z image, and a label image from an automatic explosive detection are provided, are disclosed. The method comprises generating an index image through a nonlinear transformation of the CT image, the Z image, and the label image, rotating and coloring the index image as desired, and rendering and displaying the rotated and colored image.

Abstract: A method of and a system for splitting a compound object using multi-energy CT data including a density and an atomic number measurements are provided. The method comprises: compound object detection; computing a two-dimensional DZ distribution of a compound object; identifying clusters within the DZ distribution; assigning a component label to each object voxel based on the DZ distribution clusters; and post-processing the set of voxels identified as belonging to each component.

Abstract: A method of and a system for identifying objects using histogram segment features from multi-energy CT images are provided. The multi-energy CT images include a CT image, which approximates density measurements of scanned objects, and a Z image, which approximates effective atomic number measurements of scanned objects. The method comprises: computing a density histogram for each potential threat object; smoothing the density histogram using a low-pass filter; identifying peaks in the smoothed density histogram; assigning a segment to each peak; computing histogram segment features for each segment; classifying each potential threat object into a threat or a non-threat using computed features.

Abstract: A method of and a system for variable pitch CT scanning for baggage screening and variable pitch image reconstruction are disclosed.

Abstract: A method of and a system for destreaking the photoelectric image in multi-energy computed tomography are provided, wherein the photoelectric projections are generated from the projection data acquired using at least two x-ray spectra for scanned objects; wherein a neighboring scheme is provided; the method comprises computing the statistics including mean and standard deviation using the neighboring scheme; calculating an upper limit and a lower limit from the computed statistics; detecting outliers in the photoelectric projections using the upper and lower limits; and replacing values of outliers using the upper or lower limit.

Abstract: A method of and a system for sharp object detection using computed tomography images are provided. The method comprises identifying voxels corresponding to individual objects; performing eigen-analysis and generating eigen-projection of an identified object; computing an axial concavity ratio of the identified object; computing a pointness measurement of the identified object; computing a flat area of the identified object; calculating a sharpness score of the identified object; and declaring the identified object as a threat if the sharpness score is greater than a pre-defined threshold.

Abstract: A method of and system for detecting threat objects represented in 3D CT data uses knowledge of one or more predefined shapes of the threat objects. An object represented by CT data for a region is identified. A two-dimensional projection of the object along a principal axis of the object is generated. A contour of the object boundary in the projection image is computed. A shape histogram is computed from the extracted contour. A difference measure between the extracted shape histogram and a set of pre-computed threat shape histograms is computed. A declaration of a threat object is made if the difference measure is less than a pre-defined threshold.