Analogic Corporation

General Electric Company

Andrew Litvin

Ram Naidu

Sergey Simanovsky

Ruvin Deych

Daniel Abenaim

David Lieblich

Randy Luhta

Aleksander Roshi

Mohamed Ali Hamadeh

Julia Pavlovich

Vladan Ristanovic

Ram C Naidu

Sergey B Simanovsky

Among other things, computed tomography (CT) systems and/or techniques for generating projections images of an object(s) under examination via a CT system are provided. The projection images of the object may represent a projection of the entire object or merely a portion of the object, such as a slice of the object. A surface about which the projection image is focused is defined and data yielded from a plurality of views of the object is mapped to the surface. In some embodiments, such a mapping comprises mapping data corresponding to a first view and yielded from a first detector cell to a first point on the surface, mapping data corresponding to the first view and yielded from a second detector cell to a second point on the surface, and/or mapping data corresponding to a second view and yielded from the first detector cell to a third point on the surface.

Image generation via computed tomography system

Among other things, a detector array () for a radiation imaging system is provided. The detector array comprises a plurality of detector elements. Respective detector elements comprise, among other things, a scintillator () and a photodetector (). In some embodiments, a scintillator is shared amongst two or more of the detector elements. In some embodiments, little to no reflective material, configured to mitigate cross-talk between detector elements, is situated between two or more detector elements.

Indirect conversion detector array

A detector array such as for use in a radiation imaging modality is provided. The detector array includes a first pixel () having a first scintillator (). The first scintillator has a first detection surface () and a first light emission surface (). The first detection surface extends along a first detection surface plane and the first light emission surface extends along a first light emission surface plane. The detector array includes a second pixel () having a second scintillator (). The second scintillator has a second detection surface () and a second light emission surface (). The second detection surface extends along a second detection surface plane and the second light emission surface extends along a second light emission surface plane. At least one of the first detection surface plane is not coplanar with the second detection surface plane or the first light emission surface plane is not coplanar with the second light emission surface plane.

Detector array for imaging modality

Among other things, a detection assembly of a radiation detector system is provided. In some embodiments, the detection assembly comprises a plurality of detector elements. Respective detector elements include a scintillator array, a photodetector array supporting the scintillator array on a first side of the photodetector array, and an electrical contact disposed on a second side of the photodetector array. In some embodiments, the detection assembly includes a printed circuit board. The electrical contact of respective detector elements is bonded to the printed circuit board to physically and electrically couple respective detector elements to the printed circuit board. A method of fabricating a detection assembly of a radiation detector system is also provided.

Radiation detector system of radiation imaging modality

Among other things, one or more techniques and/or systems are described for correcting projection data generated from a computed tomography (CT) examination of an object and/or for computing or updating a CT value of the object from the projection data. An image generator is configured to generate a CT image of an object under examination. Using this CT image, a set of actions are performed to correct projection data from which the CT image was generated and/or to update a CT value of one or more voxels within the CT image. In this way, the projection data and/or CT image is adjusted to reduce image artifacts and/or otherwise improve image quality and/or object detection.

Projection data correction and computed tomography value computation

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.

Iterative image reconstruction

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.

Efficient quasi-exact 3D image reconstruction algorithm for CT scanners

Among other things, computed tomography (CT) systems and/or techniques for generating projections images of an object(s) under examination via a CT system are provided. A surface about which the projection image is focused is defined and data yielded from vertical rays of radiation intersecting the surface and data yielded from non-vertical rays intersecting the surface are used to generate the projection image. In some embodiments, the projection image is assembled from one or more projection lines, which are respectively associated with a line-path contacting the surface and extend in a direction parallel to an axis of rotation for a radiation source. A projection line is indicative of a degree of attenuation experienced by rays intersecting a line-path associated with the projection line and emitted while the radiation source was at a particular segment of a radiation source trajectory.

Projection image generation via computed tomography system

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.

Determination of z-effective value for set of voxels using CT density image and sparse multi-energy data

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.

Synthetic image generation by combining image of object under examination with image of target

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.

Object identification using sparse spectral components

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.

Two-dimensional colored projection image from three-dimensional image data

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.

Compound object separation

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.

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Stoneham, MA, United States of America

Andrew Litvin