134 results about "Volumetric image" patented technology

A method is provided for optimizing workflow for a vascular intervention based on a three-dimensional dataset of a vessel, particularly a coronary artery, acquired from a 3D imaging modality, which involves:determining vessel centerline;performing segmentation in the dataset to identify the vessel lumen;performing plaque segmentation in the dataset to identifying the plaque in the vessel wall;defining a parameter related to the plaque severity in the vessel wall; andcreating a two-dimensional image spanning the length of the segmented vessel to illustrate both the plaque severity parameter and its spatial distribution in relation to the vessel wall circumference using the position of points along the centerline in the segmented vessel as primary axis.Other aspects are described and claimed.

The invention relates to a scatter correction method, a cone beam computed tomography (CBCT) system and a computer storage medium. The method comprises the following steps: performing cone beam CT imaging on a to-be-measured object to obtain a first group of projection data of the to-be-measured object, and performing reconstruction according to the first group of projection data to obtain a first volume image containing scattering artifacts; performing fan-beam CT imaging on a plurality of different heights of the to-be-measured object to obtain a plurality of groups of fan-beam projection data of the to-be-measured object, performing reconstruction according to the plurality of groups of fan-beam projection data to obtain a plurality of fan-beam slice images, and using the plurality of fan-beam slice images as reference images without scattering artifacts; extracting a plurality of cone beam slice images from the first volume image; performing image registration on the plurality of fan-beam slice images and the plurality of cone-beam slice images; and training a scattering correction model based on deep learning by using the plurality of registered image pairs to obtain a trained scattering correction model.

Systems, methods and devices are provided for performing diagnostic or therapeutic transcranial procedures using a patient-specific transcranial headset. The patient-specific headset may include a patient-specific frame that is fabricated, according to volumetric image data, to conform to an anatomical curvature of a portion of a patient's head. The patient-specific frame is configured to support a plurality of transducers in pre-selected positions and orientations, which may be spatially registered to the volumetric image data. This spatial registration may be employed to control at least a portion of the transducers to focus energy at a preselected tissue region.

The present invention discloses methods for automatically computing an arterial input function from one or more regions of interest, the method comprising: a. obtaining a plurality of dynamic image data sets comprising volumetric image data from the regions of interest over multiple scanning intervals; b. utilizing an artificial neural network to segment the plurality of dynamic image data sets displaying one or more arterial input function(s) (AIF) in the region(s) of interest; c. automatically estimating, using artificial intelligence, an arterial input function based on plurality of dynamic image data sets combined with one or more time activity curves (TAC) in the region(s) of interest in target organ(s); and d. computing a pre-trained predictive pharmacokinetic AI model arterial input function using time activity curve input associated with region(s) of interest of target organ(s).

A volumetric image of a scene can be created, in one embodiment, by recording, through a camera in a device, a series of images of the scene as the camera is moved along a path relative to the scene; during the recording, the device stores motion path metadata about the path, and the series of images is associated with the motion path metadata and a metadata label is associated with the series of images, the metadata label indicating that the recorded series of images represent a volumetric image of the scene. The series of images, the motion path metadata and the metadata label can be assembled into a package for distribution to devices that can view the volumetric image, which may be referred to as a limited volumetric image. The devices that receive the volumetric image can display the individual images in the series of images or as a video.

An apparatus includes a memory to store first volumetric image data representing a first volumetric image of an anatomical region having a volume of interest (VOI). The first volumetric image includes several first voxels at first locations in the first volumetric image. The first volumetric image data includes first uncertainty values corresponding to the several first voxels. The first uncertainty values represent probabilities that the corresponding plurality of first voxels are part of the VOI. The memory is to store second volumetric image data representing a second volumetric image of the anatomical region. The apparatus includes a processing device operatively coupled to the memory. The processing device is to determine a deformation field to map the several first voxels to second locations in the second volumetric image. The deformation field is based in part on the first uncertainty values.

The invention relates to the technical field of biomedical imaging, and discloses a photoacoustic-ultrasonic volume imaging method and system based on an annular ultrasonic array, and the method comprises the steps: reconstructing a two-dimensional photoacoustic image and a two-dimensional ultrasonic image; after photoacoustic imaging and ultrasonic synthetic aperture imaging of one imaging plane are completed, the annular ultrasonic array is moved in the direction perpendicular to the imaging plane, photoacoustic images and ultrasonic images of all the imaging planes are repeatedly executed on the next imaging plane, rebuilt and stacked, and a three-dimensional photoacoustic volume image and a three-dimensional ultrasonic volume image are formed; performing spatial pulse response deconvolution processing on the three-dimensional photoacoustic volume image; and fusing the processed three-dimensional photoacoustic volume image and the three-dimensional ultrasonic volume image to generate a three-dimensional bimodal volume image. According to the invention, high-time-resolution opto-acoustic-ultrasonic bimodal three-dimensional imaging is realized by using an annular ultrasonic array and motion scanning, and the high spatial resolution of the image is further ensured through a series of technologies.

An innovative imaging-based subject-specific whole-lung deposition model is provided. Computed tomography (CT) lung volumetric images at total lung capacity (TLC) may be used to segment airways and lobes, and registration of CT images at TLC and functional residual capacity (FRC) provided metrics of regional air volume changes. A volume-filling technique may then be used to generate the entire conducting airways and acinar units. In each acinar unit, a respiratory airway model may be generated based on existing morphometric data. The flow distributions in conducting airways and to acinar units may be calculated by a one-dimensional (ID) computational fluid dynamics (CFD) model. With the simulated airflow field, deposition fractions may be calculated using deposition probability formulae adjusted with an enhancement factor to account for the effects of transient secondary flow and realistic airway geometry.

A method for determining a dental condition comprising: receiving at least one volumetric image and at least one intra-oral surface scan image of a dental structure of a patient, wherein the dental structure includes at least one of a crown, root, gingiva or bone; aligning the at least one volumetric image with the at least one intra-oral surface scan image; identifying boundaries of the gingiva and the bone within the aligned images; and calculating distances between the identified boundary of the gingiva and cementoenamel junction (CEJ), the identified boundary of the bone and the CEJ, and the identified boundary of the gingiva and the identified boundary of the bone to determine the dental condition.

Provided is a method for labeling volumetric data, including obtaining, with a computing device, access to volumetric data in a hierarchical spatial data structure, displaying, with the computing device, a multiresolution representation of a three-dimensional image volume in an extended reality environment configured to display volumetric data using the hierarchical spatial data structure, receiving, with the computing device, a selection of a region of interest of the image volume, receiving, with the computing device, user input defining a brush trajectory within the region of interest, determining, with the computing device, which voxels within the image volume that intersect the brush trajectory within the region of interest; and labeling, with the computing device, the identified voxels in memory.

A tomographic imaging system, methods to visualize tomographic images, and a ‎stereoscopic calibration procedure for obtaining two-dimensional cross-sectional ‎and three-dimensional volumetric images over the entire anterior eye. The optical ‎system combines stereoscopic imaging with patterned illumination (e.g., slit-‎scanning or array of slits). The captured cross-sectional images can be viewed ‎individually or as a fly-through video. The calibration and reconstruction methods ‎determine the true spatial location of imaged features and record a 3D volumetric ‎representation of the anterior eye. The system can image the entire anterior ‎chamber and contiguous structures, including the corneal limbus, sclera and ‎eyelids. Furthermore, the system can obtain corneal topography. ‎

Systems and methods are provided for scanning a dental impression to obtain a digital model of a patient's dentition as an input to computer aided design (CAD) and computer aided manufacturing (CAM) methods for producing dental prostheses. Systems and methods are provided for scanning a physical impression of a patient's dentition and constructing a virtual surface image of the patient's dentition from the scan data thereby obtained. The virtual surface image of the patient's dentition is constructed using isosurfaces and density gradients of a volumetric image and then directly creating a surface image based upon void spaces that correspond to the patient's dentition.

Methods and apparatus for transmission of volumetric images in the MPI format. According to an example embodiment, texture and alpha layers of multiplane images are packed, as tiles, into a sequence of video frames. The sequence of video frames is then compressed to generate a video bitstream, which is transmitted together with a metadata bitstream specifying at least the parameters of the packing arrangement for the tiles in the sequence of video frames. Example packing arrangements include various selectable spatial and temporal arrangements for texture layers, alpha layers, and camera views. In some examples, the metadata bitstream is implemented using a SEI message and includes parameters selected from the group consisting of a size of the reference view, the number of layers in the multiplane image, the number of simultaneous views, one or more characteristics of the packing arrangement, layer merging information, dynamic range adjustment information, and reference view information.

A system and method for reviewing a tomosynthesis image data set comprising volumetric image data of a breast, the method comprising, in one embodiment, causing an image or a series of images from the data set to be displayed on a display monitor and selecting or indicating through a user interface an object or region of interest in a presently displayed image of the data set, thereby causing an image from the data set having a best focus measure of the user selected or indicated object or region of interest to be automatically displayed on the display monitor.

A plurality of scattered laser pulses is received, each scattered laser pulse associated with at least one plurality of respective dwells. A set of 3D velocity information is received, which is derived from photo events detected in information associated with the received plurality of scattered laser pulses. Each dwell in the plurality of dwells is associated with one or more photo events. The 3D velocity information includes information estimating each respective photo event's respective position in 6D space during the respective dwell associated with the photo event. For each dwell, its respective photo events are projected into a common reference frame, determined based on the 3D velocity information, to generate a set of motion-compensated point clouds. Each respective motion-compensated point cloud, for each dwell, is registered to the other motion-compensated point clouds in the set, to generate a set of registered point clouds, which are merged into a volumetric image.

A method of segmenting medical volumetric images 210 using a control input, which may be a digital brush, comprising displaying 270 a representation as a two-dimensional visualisation based upon slices (Fig.1 130) of the volume wherein a user provides input specifying a change in the plane of the slice and updating the visualisation accordingly. Further input is then made to select a target location for the segmentation via identifying a target voxel and applying segmentation based thereon. The interface may enable free movement of the position or orientation of the slice plane and may generate a visualisation based on sampled voxel values of the medical volume based on the slice plane. The brush may allow the visualisation to be coloured based upon the classification, have a variety of fill or threshold options or be user configurable. A 3D visualisation of the volume may be presented in virtual or augmented reality.

A system may be configured to generate multiple machine-learning based volumetric images from initial measurement data. Slices of a machine-learning based volumetric image may correspond to ML-based versions of respective slices of the initial volumetric image. The system may calculate differences between the different machine-learning based volumetric images. The system may enable a user to select an image slice for viewing. Based on the selection the system may identify multiple machine-learning based image slices that correspond to the selected image slice, generate a display object based on the identified machine-learning based image slices, and display the display object.

A method for identifying material boundaries in volumetric image data is provided. The method is based on the use of a model boundary transition function that models the expected progression of voxel values ​​across a material boundary as a function of distance. Each voxel is acquired in turn, and voxel values ​​in a subregion surrounding the voxel are fitted to the model function to obtain corresponding fitting parameters, in addition to parameters related to the quality of the model fit. Based on these parameters for each voxel, candidate spatial points estimated to lie on the material boundary within the 3D image dataset are identified for at least a subset of voxels. Optionally, an additional preliminary step can be applied to filter voxels to candidate voxels, for example, based on each voxel's estimated distance to the material boundary. This can be estimated based on a specific combination of model parameters for each voxel. Candidate points can be identified only for the identified candidate voxels. This results in a cloud of candidate spatial points that spatially correspond to the contour of the boundary wall. Based on these, a representation of the boundary wall (e.g., a surface mesh) can be generated.

Aligning and coloring volumetric images is disclosed, including: capturing intensity data using at least one scanner; generating an intensity image using the intensity data, wherein the intensity image includes at least one feature in a scene, the at least one feature including a sample feature; capturing image data using at least one camera, wherein the image data includes color information; generating a camera image using the image data, wherein the camera image includes the sample feature; matching the sample feature in the intensity image to the sample feature in the camera image to align the intensity image with the camera image; and generating a color image by applying the color information to the aligned intensity image.

Systems and methods for determining the health status of a retina of a subject. An optical coherence tomography (OCT) volume image of a retina of a subject may be received. A health indication output is generated, via a deep learning model, using the OCT volume image. The health indication output indicates a level of association between the OCT volume image and a selected health status category for the retina. A map output for the deep learning model is generated using a saliency mapping algorithm, generating a map output for the deep learning model using a saliency mapping algorithm. The map output indicates a level of contribution of a set of regions in the OCT volume image to the health indication output generated by the deep learning model.

An apparatus includes a memory to store volumetric data representing a volumetric image of an anatomical region having a volume of interest (VOI), and a processing device operatively coupled to the memory. The processing device is to determine, based on the volumetric data, several voxels of the volumetric image. Each voxel has a respective uncertainty value representing a probability that the voxel belongs to the VOI. The processing device is further to generate beam control data to direct a treatment beam relative to the VOI based on the uncertainty values to meet a predetermined quality metric.

A method for automated steering of magnetically steerable intravascular guidewires includes accessing, by a magnetic steering control component, volumetric imaging associated with a patient. The magnetic steering control component generates, prior to an operation on the patient, a three-dimensional vessel map based, upon the accessed volumetric imaging. The magnetic steering control component generates, prior to the operation, a path through a vasculature of the patient. The magnetic steering control component displaying, prior to the operation, in a. graphical user interface, the three-dimensional vessel map and the path. The magnetic steering control component receives input representing a modification to the path and modifies the path based upon the received input. The magnetic steering control component directs, during the operation, at least one of a medical robot arm and a motorized, guidewire advancing unit to modify a location of a magnetically steerable intravascular guidewire in accordance with the modified path.

A computer-implemented method of registering medical images, is provided. The method includes computing a spatial transformation between a first projection image (P1) and a volumetric image. The first projection image (P1) is generated from a first position (POS1) of a projection imaging system (110). Input defining the region of interest (ROI) in the volumetric image is received. One or more adjustments (TP1→P2) to the first position (POS1) of the projection imaging system are then calculated based on the spatial transformation and the received input, to provide a second position (POS2) of the projection imaging system for generating a second projection image (P2) representing the region of interest (ROI). A second projection image (P2) representing the region of interest (ROI) is then received. The second projection image (P2) is generated from the second position (POS2). The second projection image (P2) is then registered to the volumetric image (V1) based on the spatial transformation and the calculated one or more adjustments (TP1→P2).

The disclosure relates to photocleavable crosslinkers that are suitable for preparing photodegradable hydrogels and to methods for volumetric imaging of biological specimens by photochemical sectioning and imaging of photodegradable hydrogels. A photocleavable crosslinker according to the disclosure can include one or more water-soluble hydrocarbon linkers, two or more reactive functional groups such as ethylenically unsaturated groups, and one or more photocleavable groups. A crosslinked hydrogel can be formed between the photocleavable crosslinker and other hydrogel monomers. The crosslinked hydrogel can be used as a matrix immobilize a biological or other sample material in a hydrogel composite. The hydrogel composite and the sample material therein can be volumetric imaged by sequentially and repeatedly optically imaging an image layer of the hydrogel composite and then photochemically sectioning a depolymerization layer of the hydrogel composite.

A CSF management method is used with a patient having a body with CSF having a natural flow rate. The method determines determining a target volume for a CSF-containing target compartment of a patient from a volumetric image. The method determines a personalized therapeutic infusion volume of a therapeutic material as a function of the target volume and a total therapeutic infusion volume. A CSF circuit is formed to control flow of CSF in the body. The personalized therapeutic infusion volume is added to the CSF via the CSF circuit at a first time. The therapeutic material is directed, via the CSF, toward the target compartment of the patient. The flow of the CSF in the CSF circuit is adjusted to localize the CSF at the target compartment of the patient.

Determining a patient-level clinical endpoint prediction based on analysis of a three-dimensional volumetric image is discussed. One example method includes generating a set of patches from a volumetric image of a tissue sample. The method also includes employing a pretrained feature encoder to extract a set of features from the set of patches. The method additionally includes generating a volume-level feature associated with the volumetric image via an aggregation based on the set of features. The method further includes generating a clinical endpoint prediction based on the volume-level feature.

A method and system for generating a 3D volumetric image. The method and system comprise rotating about a rotation axis a light modulating surface comprising separately controllable light modulating regions located across the light modulating surface and controlling separately the modulation of the light modulating regions to generate the 3D volumetric image upon rotation of the light modulating surface about the rotation axis. A characteristic of two or more selected light modulating regions of the light modulating surface is then varied in accordance with respective spacings of the two or more selected light modulating regions from the rotation axis.

The system includes a processor and a display. The processor is configured to (i) receive a first three-dimensional (3D) ultrasound (US) image of a volume of a patient's organ, (ii) present at least first and second two-dimensional (2D) slices selected in the first 3D US image, (iii) receive first and second 2D contours including a region of interest (ROI) in the volume of the organ selected in the first and second 2D slices, respectively, and (iv) generate a second 3D US image of the ROI based on the first and second 2D contours. The display is configured to present the second 3D US image to a user.

A method for identifying a material boundary within volumetric image data is based on use of a model boundary transition function, which models the expected progression of voxel values across the material boundary, as a function of distance. Each voxel is taken in turn, and voxel values within a subregion surrounding the voxel are fitted to the model function, and the corresponding fitting parameters are derived, in addition to a parameter relating to quality of the model fit. Based on these parameters for each voxel, for each of at least a subset of the voxels, a candidate spatial point is identified, estimated to lie on the material boundary within the 3-D image dataset. The result is a cloud of candidate spatial points which spatially correspond to the outline of the boundary wall. Based on these, a representation of the boundary wall can be generated, for example a surface mesh.

The present application relates to volumetric OCT image data processing. A method of processing C-scan data to generate correction data, the C-scan data comprising a sequence of B-scans of an imaging target acquired by an OCT imaging system, the correction data being used to compensate for axial displacement between B-scans in the sequence caused by relative motion between the OCT imaging system and the imaging target, the relative motion causing a change in the distance between the OCT imaging system and the imaging target during acquisition of the B-scans, the correction data being generated by: determining (S10) for each of a plurality of adjacent B-scan pairs in the sequence a respective indicator of axial displacement between respective representations of a common ocular feature in the adjacent B-scans; and determining (S20) based on a change in the determined indicator as a function of position of the corresponding adjacent B-scan pair in the sequence, a first frequency component of the change in the change in the frequency component being indicative of the relative motion during acquisition of the B-scans.