General Electric Company

University System of Maryland

The Cleveland Clinic Foundation

Vivek Walimbe

Raj Shekhar

Romain Areste

Kadri Nizar Jabri

Rowland F Saunders

Uwe Wiedmann

Floribertus P M Heukensfeldt Jansen

Mark Alan Frontera

Yun Zou

Jon Charles Omernick

Scott Stephen Zelakiewicz

Xiao Xuan

Floribertus Pm Heukensfeldt Jansen

Vladimir Zagrodsky

Rowland Frederick Saunders

In accordance with one aspect of the present system, an X-ray detector of an X-ray imaging system includes a communication module configured to receive a pre-shot image from a detection circuitry and receive one or more pre-shot parameters from a source controller of the X-ray imaging system. The X-ray detector further includes an analysis module configured to determine one or more image characteristics of the pre-shot image. The X-ray detector further includes a determination module configured to calculate one or more main-shot parameters based on the one or more pre-shot parameters and the one or more image characteristics. The determination module is further configured to send the one or more main-shot parameters to the source controller of the X-ray imaging system.

System and method for determining X-ray exposure parameters

A method and apparatus for image automatic brightness detection. The method includes determining region of interest (ROI) candidates in an image, extracting features from each of the ROI candidates, selecting the optimum ROI based on weighted score of each of the ROI candidate, and calculating brightness value of the selected optimum ROI candidate as brightness feedback. The method and apparatus for image automatic brightness detection according to embodiments of the present invention, can automatically detect the point of interest for clinicians, and then provide a more accurate feedback to the imaging system, in order to provide a more efficient dose management in the imaging system and thus to achieve constant image quality without wasting any dose, thereby further optimizing the dose/IQ performance and the high efficient utilization of the system.

Method and apparatus for image content-based automatic brightness detection

A radiography system allow for user determination of a region of interest on a subject prior to X-ray exposure. The region of interest is defined by user interaction with an image, a pointer system, or the like. The region of interest is then translated to the imaging coordinate system, such as in the plane of a digital detector. The region is then used for exposure control during an imaging sequence, either in an open or closed-loop manner.

Region of interest determination for X-ray imaging

Fluoroscopy imaging systems including an X-ray generator, a detector, and control circuitry coupled to the detector are provided. The control circuitry may be adapted to receive a digital signal from the detector, to process the digital signal, and to communicate the processed digital signal to a monitor for display during a fluoroscopy imaging operation. The fluoroscopy imaging systems may also include a user interface having at least one configurable adjustment configured to enable a user to adjust one or more imaging parameters affecting the digital signal during the fluoroscopy imaging operation. The control circuitry is adapted to set at least one of a range of the configurable adjustment, a step size of the configurable adjustment, or a default value of the configurable adjustment based on input from the user via the user interface.

Fluoroscopy systems and methods

Methods and apparatus for generating and archiving x-ray fluoroscopy images are provided. The method includes obtaining x-ray fluoroscopy data comprising a plurality of image frames and performing image enhancement, including motion correction, based on a subset of the acquired plurality of x-ray fluoroscopy image frames to generate a single enhanced x-ray image.

Methods and apparatus for generating and archiving x-ray fluoroscopy images

Techniques for registration of multiple measurement modes of a body include receiving first and second data from different modes. Each includes measured values with coordinate values. For two mechanically aligned modes, any non-rigid registration is performed. For some modes, the non-rigid registration includes a coarse transformation and multiple fine scale transformations. The coarse transformation maximizes a coarse similarity measure. The second data is sub-divided into contiguous sub-regions. Fine transformations are determined between the sub-regions and corresponding portions of the first data to maximize a fine similarity measure. Sub-dividing and determining fine transformations repeats until stop conditions are satisfied. Transformations between the last-divided sub-regions are interpolated. Any of the fine similarity measure, a search region, interpolation method, sub-division location, and the use of rigid or non-rigid fine transformations are adaptive to properties of the first or second data so that the registration is automatic without human intervention.

Techniques for 3-D elastic spatial registration of multiple modes of measuring a body

Stress test analysis is facilitated through the acquired and manipulated use of a sequence of volumetric data regarding the heart (and may particularly comprise the left ventricle) for the assessment of the health state of the heart. Several provided and illustrated examples specifically relate to ultrasound volumetric data, but the volumetric data may be obtained through the use of any imaging modality (e.g., CT, MRI, X-ray, PET, SPECT, etc.) or combination thereof, and may be used to compute one or more functional quantitative metrics (e.g., ejection fraction.) The volumetric data may also be used to render one or more views of the heart, and particularly of the left ventricle. This disclosure relates to these and other uses of such volumetric data, and to some various implementations thereof, such as methods, systems, and graphical user interfaces.

Quantitative real-time 4D stress test analysis

Techniques for registration of multiple measurement modes of a body include receiving first and second data from different modes. Each includes measured values with coordinate values. For two mechanically aligned modes, any nonrigid registration is performed. For some modes, the nonrigid registration includes a coarse transformation and multiple fine scale transformations. The coarse transformation maximizes a coarse similarity measure. The second data is subdivided into contiguous subregions. Fine transformations are determined between the subregions and corresponding portions of the first data to maximize a fine similarity measure. Subdividing and determining fine transformations repeats until stop conditions are satisfied. Transformations between the last divided subregions are interpolated. Any of the fine similarity measure, a search region, interpolation method, sub-division location, and the use of rigid or non-rigid fine transformations are adaptive to properties of the first or second data so that the registration is automatic without human intervention.

Techniques for segmenting data include receiving reference segmentation data and target data. The reference segmentation data defines a mesh indicating a boundary of a physical component in a reference body. The target data includes measured values at coordinates within a target body. Coordinates for vertices of the mesh are moved toward nearby edges in values of the target data. The adjustment also may be based on deviations from adjacent vertices or from a three dimensional generalized gradient vector field. The mesh may be initially transformed by a particular transformation that automatically maximizes a similarity measure between the target data and reference data. The reference data includes measured values within the reference body. The reference segmentation also may define a second mesh that indicates a second boundary in the reference body, and the adjustment is also based on an adjusted distance between the mesh and the second mesh.

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Pewaukee, WI, United States of America

Vivek Walimbe