Abstract: Z-effective (e.g., atomic number) values are generated for one or more sets of voxels in a CT density image using sparse (measured) multi-energy projection data. Voxels in the CT density image are assigned a starting z-effective value, causing a CT z-effective image to be generated from the CT density image. The accuracy of the assigned z-effective values is tested by forward projecting the CT z-effective image to generate synthetic multi-energy projection data and comparing the synthetic multi-energy projection data to the sparse multi-energy projection data. When the measure of similarity between the synthetic data and the sparse data is low, the z-effective value assigned to one or more voxels is modified until the measure of similarity is above a specified threshold (e.g., with an associated confidence score), at which point the z-effective values substantially reflect the z-effective values that would be obtained using a (more expensive) dual-energy CT imaging modality.

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 can comprise two or more separate sub-objects, producing a compound object. Compound objects can affect the quality of object visualization and threat identification. As provided herein, a compound object can be separated into sub-objects based on object morphological properties (e.g., an object's shape, surface area). Further, a potential compound object can be split into sub-objects, for example, eroding one or more outer layers of volume space (e.g., voxels) from the potential compound object. Additionally, a volume of a representation of the sub-objects in an image can be reconstructed, for example, by generating sub-objects that have a combined volume approximate to that of the compound object. Furthermore, sub-objects, which can be parts of a same physical object, but may have been erroneously split, can be identified and merged using connectivity and compactness based techniques.

Abstract: Among other things, one or more techniques and/or systems for combining a three-dimensional image of a target with a three-dimensional image of an object that is under examination via radiation to generate a three-dimensional synthetic image are provided. Although the target is not actually comprised within the object under examination, the three-dimensional synthetic image is intended to cause the target to appear to be comprised within the object. In one embodiment, one or more artifacts may be intentionally introduced into the three-dimensional synthetic image that are not comprised within the three-dimensional image of the target and/or within the three-dimensional image of the object to generate a synthetic image that more closely approximates in appearance a three-dimensional image that would have been generated from an examination had the target been comprised within the object.

Abstract: One or more techniques and/or apparatuses described herein provide for reconstructing image data of an object under examination from measured projection data indicative of the object. The measured projection data is converted into image data using an iterative image reconstruction approach. The iterative image reconstruction approach may comprise, among other things, regularizing the image data to adjust a specified quality metric of the image data, identifying regions of the image data that represent aspects of the object that might generate inconsistencies in the measured projection data and correcting the measured projection data based upon such an identification, and/or weighting projections comprised in the measured projection data differently to reduce the influence of projections that respectively have a higher degree of inconsistency in the conversion from projection data to image data.

Abstract: Representations of an object 110 in an image generated by an imaging apparatus 100 can comprise one or more potential compound objects 500, where a compound object comprises two or more separate sub-objects. Compound objects can negatively affect the quality of object visualization and/or make identifying threat objects more difficult, for example. Accordingly, as provided herein, a representation of a potential compound object 500 can be examined for separation into sub-objects. To do so, three-dimensional image data of a potential compound object 500 is projected to generate one or more Eigen projections 504, and segmentation is performed on the two-dimensional Eigen projection(s) to identify sub-objects. Once sub-objects are identified, the segmented Eigen projection(s) 900 is back-projected into three-dimensional space 1104 for further processing, for example.

Abstract: One or more systems and/or techniques are provided to identify and/or classify objects of interest (e.g., potential granular objects) from a radiographic examination of the object. Image data of the object is transformed using a spectral transformation, such as a Fourier transformation, to generate image data in a spectral domain. Using the image data in the spectral domain, one or more one-dimensional spectral signatures can be generated and features of the signatures can be extracted and compared to features of one or more known objects. If one or more features of the signatures correspond (e.g., within a predetermined tolerance) to the features of a known object to which the feature(s) is compared, the object of interest may be identified and/or classified based upon the correspondence.

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: A CT scanner comprises: at least one source of X-rays and a multi-row detector array of arbitrary geometry, both supported so as rotate around an axis of rotation during a scan of an object translated along the axis, wherein data for each detector is generated as a function of the X-ray energy received; and a data processor configured so as to perform resampling of the data onto curves in a virtual detector array. The curves project onto tilted lines in a virtual flat detector as to enable tangential filtering of the data.

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: Disclosed is a method and apparatus for registering data points in data sets representing scan data. Data points corresponding to a physical feature represented in the scan data are automatically detected. The detected data points in one data set are correlated with detected data points in another data set. A group of similarity transformations between the correlated detected data points is then calculated. The group of similarity transformations is then combined. In one advantageous embodiment, the physical feature is vertebras.

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: Provided is a method of image-guided navigation comprising determining a final navigated point in a three-dimensional reconstructed coordinate system based at least partially on two or more navigated points in a coordinate system associated with a two-dimensional array of image pixels and without specifying a coordinate of each of the two or more navigated points in a direction orthogonal to the two-dimensional array of image pixels, wherein each of the two or more navigated points is for a different position of a C-arm gantry. Also provided is an image-guided navigation system.

Abstract: Representations of an object can comprise two or more separate sub-objects, producing a compound object. Compound objects can affect the quality of object visualization and threat identification. As provided herein, a compound object can be separated into sub-objects based on object morphological properties (e.g., an object's shape, surface area). Further, a potential compound object can be split into sub-objects, for example, eroding one or more outer layers of volume space (e.g., voxels) from the potential compound object. Additionally, a volume of a representation of the sub-objects in an image can be reconstructed, for example, by generating sub-objects that have a combined volume approximate to that of the compound object. Furthermore, sub-objects, which can be parts of a same physical object, but may have been erroneously split, can be identified and merged using connectivity and compactness based techniques.

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: Provided is a method of image-guided navigation comprising determining a final navigated point in a three-dimensional reconstructed coordinate system based at least partially on two or more navigated points in a coordinate system associated with a two-dimensional array of image pixels and without specifying a coordinate of each of the two or more navigated points in a direction orthogonal to the two-dimensional array of image pixels, wherein each of the two or more navigated points is for a different position of a C-arm gantry. Also provided is an image-guided navigation system.

Abstract: Disclosed is a method and apparatus for registering data points in data sets representing scan data. Data points corresponding to a physical feature represented in the scan data are automatically detected. The detected data points in one data set are correlated with detected data points in another data set. A group of similarity transformations between the correlated detected data points is then calculated. The group of similarity transformations is then combined. In one advantageous embodiment, the physical feature is vertebras.

Abstract: A system and method for detecting the spinal cord in thoracic and abdominal computed tomography (CT) volume data is disclosed. In this method, an initial spinal cord point is detected for an initial axial slice of the CT volume data. The spinal cord is then tracked from the initial spinal cord point across each remaining axial slices in opposite directions from the initial axial slice by sequentially detecting a spinal cord point for each of the remaining axial slices The initial spinal cord point is detected using a ring model based on intensity differences in the initial axial slice. For each remaining axial slice, a spinal cord center position based on at least one previously detected spinal cord point. The spinal cord points on each of the remaining axial slices are detected based on intensity differences in the axial slice as well as proximity to the predicted spinal cord center position for the axial slice.