Abstract: An example method may include acquiring images from cameras, each having a known position and orientation with respect to a spatial coordinate system of an augmented reality (AR) device. The acquired images may include portions of a multi-modal marker device that includes at least one tracking sensor having a three-dimensional position that is detectable in a coordinate system of a tracking system. A three-dimensional position is estimated for the portions of the multi-modal marker device with respect to the spatial coordinate system of the AR device based on each of the respective acquired images and the known position and orientation of the cameras with respect to the spatial coordinate system of the AR device. The method also includes computing an affine transform configured to register the coordinate system of the tracking system with a visual space of a display that is in the spatial coordinate system of the AR device.

Abstract: An example method may include acquiring images from cameras, each having a known position and orientation with respect to a spatial coordinate system of an augmented reality (AR) device. The acquired images may include portions of a multi-modal marker device that includes at least one tracking sensor having a three-dimensional position that is detectable in a coordinate system of a tracking system. A three-dimensional position is estimated for the portions of the multi-modal marker device with respect to the spatial coordinate system of the AR device based on each of the respective acquired images and the known position and orientation of the cameras with respect to the spatial coordinate system of the AR device. The method also includes computing an affine transform configured to register the coordinate system of the tracking system with a visual space of a display that is in the spatial coordinate system of the AR device.

Abstract: An example method includes acquiring a first image using a medical imaging modality that includes a two-dimensional field of view to include a patient and a multi-modal marker. A second image is acquired using the medical image modality. The second image includes the patient and the multimodal marker and is along a non-coincident angle with respect to the first image. Predetermined portions of the multi-modal marker are visible in the first and second images and have a known location and orientation with respect to at least one sensor detectable by a tracking system. The method also includes estimating a three-dimensional position for predetermined portions of the multi-modal marker. The method also includes determining an affine transformation for registering a three-dimensional coordinate system of the tracking system with a three-dimensional coordinate system of the medical imaging modality.

Abstract: In an example, a method can include segmented image volume data for at least one anatomic structure, wherein the segmented image volume data includes a 3-D image volume that includes the at least one anatomic structure. The method can include searching outward from a computed centerline for the at least one anatomic structure to detect a surface of one or more regions of interest of the at least one anatomic structure by evaluating values associated with voxels representing the 3-D image volume that includes the at least one anatomic structure relative to a threshold. The method can further include isolating the one or more regions of interest of the at least one anatomic structure in response to the searching and generating a region of interest model based on the one or more isolated regions of interest.

Abstract: An example method includes acquiring a first image using a medical imaging modality that includes a two-dimensional field of view to include a patient and a multi-modal marker. A second image is acquired using the medical image modality. The second image includes the patient and the multimodal marker and is along a non-coincident angle with respect to the first image. Predetermined portions of the multi-modal marker are visible in the first and second images and have a known location and orientation with respect to at least one sensor detectable by a tracking system. The method also includes estimating a three-dimensional position for predetermined portions of the multi-modal marker. The method also includes determining an affine transformation for registering a three-dimensional coordinate system of the tracking system with a three-dimensional coordinate system of the medical imaging modality.

Abstract: An example method may include acquiring images from cameras, each having a known position and orientation with respect to a spatial coordinate system of an augmented reality (AR) device. The acquired images may include portions of a multi-modal marker device that includes at least one tracking sensor having a three-dimensional position that is detectable in a coordinate system of a tracking system. A three-dimensional position is estimated for the portions of the multi-modal marker device with respect to the spatial coordinate system of the AR device based on each of the respective acquired images and the known position and orientation of the cameras with respect to the spatial coordinate system of the AR device. The method also includes computing an affine transform configured to register the coordinate system of the tracking system with a visual space of a display that is in the spatial coordinate system of the AR device.

Abstract: In an example, a method can include segmented image volume data for at least one anatomic structure, wherein the segmented image volume data includes a 3-D image volume that includes the at least one anatomic structure. The method can include searching outward from a computed centerline for the at least one anatomic structure to detect a surface of one or more regions of interest of the at least one anatomic structure by evaluating values associated with voxels representing the 3-D image volume that includes the at least one anatomic structure relative to a threshold. The method can further include isolating the one or more regions of interest of the at least one anatomic structure in response to the searching and generating a region of interest model based on the one or more isolated regions of interest.

Abstract: Flow information (e.g., velocity and acceleration) and/or pressure gradient information are determined with ultrasound. Both velocity and acceleration are simultaneously estimated using a model and least squares analysis. Anti-aliasing of velocity information may be provided using the model and least squares analysis. Pressure gradient is calculated from velocity information automatically, more likely providing consistent measurements. Consistency may be increased further by automatically positioning a region of interest in either a multidimensional spatial image or a spatial-temporal image. A parametric image of pressure gradient for each spatial location within the image is generated as well as a delta pressure curve. Any single one or combinations of two or more of the features described above may be used.

Abstract: A method and system are provided for displaying a measurement associated with an anatomical feature. The method includes determining, from ultrasound data, a measurement of such feature and assigning a color based on a comparison between the measurement and reference data of such feature. The feature may be, for example, an artery and the measurement the intima media thickness of the artery. In such example, the method and apparatus display vessel wall thickness from ultrasound imaging data and includes determining from the ultrasound imaging data, intima media thickness along the vessel wall and assigning a color based on the intima media thickness determined and a reference intima media thickness.

Abstract: Flow information (e.g., velocity and acceleration) and/or pressure gradient information are determined with ultrasound. Both velocity and acceleration are simultaneously estimated using a model and least squares analysis. Anti-aliasing of velocity information may be provided using the model and least squares analysis. Pressure gradient is calculated from velocity information automatically, more likely providing consistent measurements. Consistency may be increased further by automatically positioning a region of interest in either a multidimensional spatial image or a spatial-temporal image. A parametric image of pressure gradient for each spatial location within the image is generated as well as a delta pressure curve. Any single one or combinations of two or more of the features described above may be used.

Abstract: Motion of a region of interest is tracked in medical imaging. For example, velocity information, such as colored Doppler velocity estimates independent of tracking from one image to another image, are used to indicate an amount of motion between the images. The velocity assisted tracking may be used for one dimensional tracking (e.g., for M-mode images), for tracking in two dimensional images, or for tracking between three dimensional representations. As an alternative or additional example, physiological signal information is used to assist in the tracking determination. A physiological signal may be used to model the likely movement of an organ being imaged to control or adjust matching search patterns and limits. The modeling may also be used to independently determine movement or for tracking. The independently determined tracking is then combined with tracking based on medical data or other techniques.