Analogic Corporation

Bk Medical Holding Company, Inc.

Ram Naidu

Sergey Simanovsky

Zhengrong Ying

Carl R Crawford

Andrew Litvin

Julia Pavlovich

David A Schafer

Matthew Waggener Hirsch

Aleksander Roshi

Chitra Subramanian

Ibrahim M Bechwati

Sorin Marcovici

Spiros Mantzavinos

Eric Francis Zanin

Baoming Hong

Ruvin Deych

Daniel Abenaim

Douglas Q Abraham

David Lieblich

Basak Ulker Karbeyaz

Dong-Yueh Liang

Steve Urchuk

Jason Plante

Seemee S Karimi

Serge Savard

A method includes registering a region of interest in 3-D imaging data with an initial ultrasound image so that the region of interest is in an imaging plane of the initial ultrasound image. The method further includes acquiring a subsequent ultrasound image with a transducer array. The method further includes comparing the initial ultrasound image and the subsequent ultrasound image. The method further includes steering at least one of the transducer array or an instrument based on a result of the comparing so that at least one of the region of interest or the instrument is in the imaging plane.

Image guided steering of a transducer array and/or an instrument

A method includes receiving an intra-procedure image of a region of interest of an object. The intra-procedure image includes a representation of an instrument in the object. The method further includes identifying a location of the instrument in the object in the intra-procedure image. The method further includes combining the intra-procedure image with a pre-procedure image of the region of interest. The pre-procedure image includes a planned trajectory for the instrument from a surface of the object to a target in the object and segmented structure(s) to be avoided by the instrument during the procedure. The method further includes segmenting the same structure(s) in the intra-procedure image relative to the instrument using the segmented structure(s) in the pre-procedure image. The method further includes computing a risk of the instrument contacting the structure(s) along the trajectory with the identified location, the trajectory, and the segmented structure(s) in the intra-procedure image.

Real-time image based risk assessment for an instrument along a path to a target in an object

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. Three-dimensional image data of a potential compound object is projected into a two-dimensional manifold projection, and segmentation is performed on the two-dimensional manifold projection of the compound object to identify sub-objects. Once sub-objects are identified, the two-dimensional, segmented manifold projection is projected into three-dimensional space. A three-dimensional segmentation may then be performed to identify additional sub-objects of the compound object that were not identified by the two-dimensional segmentation.

Compound object separation

Among other things, one or more systems and/or techniques for segmenting a representation of a sheet object from an image are provided herein. To identify elements of an image (e.g., pixels and/or voxels) representative of sheet objects, a constant false alarm rate (CFAR) score and a topological score are computed for respective elements being analyzed. The CFAR score indicates a relationship between an element and a neighborhood of elements when viewed as a collective unit. The topological score indicates a relationship between the element and a neighborhood of elements when viewed neighbor-by-neighbor. When the CFAR score is within a specified range of CFAR scores and the topological score is within a specified range of topological scores, the element is labeled as being associated with a sheet object. A connected component labeling (CCL) approach may be used to group elements labeled as being associated with a sheet object.

Segmentation of sheet objects from image generated using radiation imaging modality

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 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.

CT scanning systems and methods using multi-pixel x-ray sources

Among other things, radiation systems and techniques for generating volumetric data and projections images of an object(s) under examination are provided. The radiation system comprises at least two detector arrays and at least one radiation source. During an examination, the radiation source and at least one detector array are rotated about the object while a second detector array is substantially fixed in place. In one embodiment, the radiation source is configured to, at times, illuminate the first detector array and, at times, illuminate the second detector array. For example, the radiation source may illuminate the first detector array during nearly all of the rotation while the second detector array is merely illuminated at a single gantry rotation angle. Information generated by the second detector array may be utilized to yield volumetric data while information generated by the first detector array may be utilized to yield a projection image, for example.

Volumetric and projection image generation

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, one or more systems and/or techniques for identifying an occlusion region in an image representative of an object subjected to examination is provided for herein. Such systems and/or techniques may find particular application in the context of object recognition analysis. An image is generated of the object and an orientation of the object is determined from the image. Based upon the determined orientation of the object relative to the direction the object is translated during examination, one or more parameters utilized for segmenting a second image of the object, identifying features in the image, and/or determining if the image comprises an occlusion region may be adjusted. In this way, the parameters utilized may be a function of the determined orientation of the object, which may mitigate false positives of detected occlusion regions.

Automatic occlusion region identification using radiation imaging modality

Multi-energy imaging is afforded with a single detector array by generating a first data set, indicative of a first radiation energy spectrum, using a first set of cells of the array, and by generating a second data set, indicative of a second radiation energy spectrum, using a second set of cells of the array (e.g., where substantially more cells are in the first set than the second). The first data set is comprised of measured data from the first set of cells and estimated data that would have been yielded from the second set of cells had the second set been configured to detect the first energy spectrum. The second data set is comprised of measured data yielded from the second set of cells and estimated data that would have been yielded from the first set of cells had the first set been configured to detect the second energy spectrum.

Estimating multi-energy data in a radiation imaging modality

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. Three-dimensional image data of a potential compound object () is projected into a two-dimensional manifold projection (), and segmentation is performed on the two-dimensional manifold projection of the compound object to identify sub-objects. Once sub-objects are identified, the two-dimensional, segmented manifold projection () is projected into three-dimensional space (). A three-dimensional segmentation may then be performed to identify additional sub-objects of the compound object that were not identified by the two-dimensional segmentation.

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.

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Newton, MA, United States of America

Ram Naidu