176 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.

Systems and methods are provided for performing transcranial diagnostic procedures using a transcranial ultrasound transducer array. The array elements are positioned and oriented such that far field regions respectively associated therewith spatially overlap within the brain of a patient. The array elements may be oriented approximately normal to the skull, permitting efficient coupling of ultrasound energy into the brain. The array elements are controlled to generate ultrasound pulses, where the timing of the pulses is controlled, based on registration between the array elements and volumetric image data, such that ultrasound energy is focused at a target within spatially overlapping far fields of the array elements. The transcranial ultrasound transducer array elements may be positioned and oriented relative to the skull such that their respective ultrasound beams are focused within the skull and diverging with the brain.

Methods and apparatus for quantifying an injected volume in an area of tissue during a medical procedure. In one example, a method includes determining a pixel-to-size scale parameter based on a pixelated image of a reference object included in a FOV of an optical instrument configured to perform volumetric imaging, the FOV further including an injection site; computing a first 3D model of the injected volume in the area of tissue based on a volumetric image of the FOV obtained using the optical instrument; refining the first 3D model to obtain a second 3D model of the injected volume, the refining including correcting a shape and a size of the first 3D model to reduce distortions associated with light refraction at a boundary of the injected volume in the area of tissue; and calculating a value of the injected volume based on the second 3D model and further based on the pixel-to-size scale parameter.

Methods and systems are provided for improving image quality without increasing dose of contrast agent. The method comprises: (a) receiving an input image comprising a pre-contrast image and a full-dose image, the pre-contrast image is a volumetric medical image of a subject acquired without administering contrast agent and the full-dose image is a volumetric image of the subject acquired with standard dose of contrast agent; (b) selecting a path from a plurality of paths to process the input image, the path comprises at least a first model trained to predict a contrast-enhanced image, and a second model trained to denoise an image and wherein the first model and the second model are arranged in a predetermined order to process the input image; generating a predicted image by processing the input image using the path selected in (b), the predicted image has an image quality improved over the input image.

A computer tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (116) configured to detect x-ray radiation and generate a signal indicative thereof, and a reconstructor (118) configured to reconstruct the signal and generate sequential spares time line perfusion volumetric image data. The computer tomography scanner further includes a processor (132) configured to process the sequential spares time line perfusion volumetric image data using a trained neural network of a perfusion data enhancing module (136) to produce sequential dense time line perfusion volumetric image data.

A system for 3D medical image segmentation includes a medical imaging device for obtaining a plurality of 2D images forming a volumetric image, processing circuitry, and a display. The processing circuitry is configured with a first stage to divide the volumetric image into 3D image patches, a hierarchical encoder-decoder structure in which resolution of features of the 3D image patches is decreased by a factor of two in each of a plurality of stages of the encoder, an encoder output connected to the decoder via skip connections, and a convolutional block to produce a voxel-wise final segmentation mask. The encoder includes a plurality of efficient paired attention blocks each with a spatial attention branch and a channel attention branch that learn respective spatial and channel attention feature maps. The display displays the final segmentation mask.

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 and methods for generating a 3D volumetric image from 2D X-ray images include receiving data representing at least one X-ray image of a region of interest as input data. Volume features are extracted from the X-ray image(s) and the extracted volume features are concatenated with a noise source to generate a noisy target volume. An iterative denoising process is performed on the noisy target volume by sampling the volume with a trained conditioned diffusion model. The method then reprojects the 3D volume of the region of interest. When the 2D X-ray images include pose data, the reprojecting operating reprojects the 3D volume with the pose data.

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.

The invention provides, in some aspects, a system for implementing a rule derived basis to display volumetric image sets. In various embodiments of the invention, the selection of the images to be displayed, the generation of the 3-D volumetric image from measured 2-D images including the rendering parameters and styles, the choice of viewing directions and 2-D projection images based on the viewing directions, the layout of the projection images, and the formation of a video can be determined using a rule derived basis. In an embodiment of the present invention, the user is presented with sequential images making up a video displayed based on their preferences without having to first manually adjust parameters. The present invention allows for novel ways of viewing such images to detect microcalcifications and obstructions when reviewing Digital Breast Tomosynthesis and other volumetric mammography images.

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. ‎

An imaging system includes a laparoscopic camera configured to capture a video stream of tissue and an intraoperative imaging device configured to be inserted through an access port and to obtain a plurality of signals from the tissue. The system also includes an image processing device configured to: generate a 3D reconstruction of the surgical site from the laparoscopic camera video stream to estimate a 3D position of the intraoperative imaging device in a reference frame of the laparoscopic camera; and positioning the laparoscopic camera and the intraoperative imaging device in a world coordinate system based on the 3D reconstruction of the surgical site. The image processing device is further configured to receive a volumetric image of tissue formed by the preoperative imaging modality and to generate a multi-frame representation from the plurality of signals from the intra-operative imaging device. The image processing device is further configured to: register the multi-frame representation with a volumetric image of tissue; deforming the volumetric image of the tissue according to the multi-frame representation; and generating an overlay of the volumetric image of the tissue and the multi-frame representation. The system further includes a screen configured to display a video stream showing data based on the plurality of signals from the intraoperative imaging device and an overlay extending from the intraoperative imaging device.

Temporal interleaving of therapeutic X-rays and imaging X-rays during an image guided radiation therapy (IGRT) procedure in a radiation therapy system prevents scatter of the therapeutic X-rays from degrading the quality of an X-ray image used to generate volumetric image data of a target volume. Imaging X-rays are delivered to the target volume during one or more imaging intervals, and one or more pulses of therapeutic X-rays are delivered to the target volume between the imaging intervals. Where a pulse of X-ray therapy is timed to occur during an imaging interval, the pulse of therapeutic X-ray is inhibited from occurring during the imaging interval and is rescheduled to occur at a later time that does not coincide with the imaging interval or a subsequent imaging interval.

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.

Aligning and coloring a volumetric image, 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 with the sample feature in the camera image to align the intensity image and the camera image; and generating a color image by applying the color information to the aligned intensity image.

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.