237 results about "Volumetric image" patented technology

A computer-implemented method of providing a temporal sequence of 3D angiographic images (110) representing a flow of a contrast agent through a region of interest (120), is provided. The method includes: inputting (S130) volumetric image data (130a, 130b), and a temporal sequence of 2D angiographic images (140) into a neural network (NN1); and generating (S140) the predicted temporal sequence of 3D angiographic images (110) representing the flow of the contrast agent through the region of interest (120) in response to the inputting.

An image processing apparatus (information processing apparatus) includes a volumetric image shooting unit (first acquisition unit) that acquires a plurality of real images respectively captured by a plurality of cameras (first imaging devices) arranged around a subject, a volumetric image generation unit (generation unit) that generates a 3D model of the subject from the plurality of real images, and an in-studio image display unit (presentation unit) that presents, to the subject, virtual viewpoint information used in rendering, the 3D model, into an image in a form corresponding to a viewing device.

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 method and system for detecting nascent geographic atrophy. An optical coherence tomography (OCT) volume image of a retina of a subject is received. Using a deep learning model, an output is generated using the OCT volume image in which the output indicates whether nascent geographic atrophy is detected. A map output is generated for the deep learning model using a saliency mapping algorithm, wherein the map output indicates a level of contribution of a set of regions in the OCT volume image to the output generated by the deep learning model.

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.

Methods and systems are provided for assessing occlusion of a blood vessel of interest from a volumetric image dataset. The volumetric image dataset is analyzed to extract data representing an axial trajectory of the blood vessel of interest. Generating a multi-plane reformatted (MPR) image and data representing an axial trajectory of the blood vessel of interest; the MPR image is supplied as input to a first machine learning network that outputs feature data characterizing a plurality of features of the blood vessel of interest along an axial trajectory of the blood vessel of interest. Additional data characterizing at least one additional feature of the blood vessel of interest along an axial trajectory of the blood vessel of interest is generated. And inputting the data output by the first machine learning network and the additional data into a second machine learning network, wherein the second machine learning network outputs data representing the severity of the anatomical lesion of the blood vessel of interest.

Apparatuses and computer-implemented methods for compression and extraction of three-dimensional (3D) volumetric imaging data, including in particular medical and dental imaging data, that may receive the 3D volumetric image data and preparing compressed 3D volumetric image data for compression by analyzing a relevant region of the 3D volumetric image data. The method may further include compressing, into compressed 3D volumetric image data, the pre-processed static 3D volumetric image data using a video compression scheme. Various other methods, systems, and computer-readable media are also disclosed.

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.

X-ray sources emit X-rays into an examination region along different projection views. An X-ray detector array detects the X-rays emitted by the X-ray sources after passing through the examination region. X-ray imaging data are acquired by cycling through the X-ray sources with each step of the cycle including: switching an active X ray source on to emit X-rays and the other X ray sources off to not emit X rays, and acquiring X ray imaging data along the projection view corresponding to the active X-ray source that is switched on. Tomosynthesis image reconstruction is performed on the X ray imaging data to generate at least one volumetric image. A time sequence of spatially aligned images is produced from the time sequence of volumetric images. A perfusion image and / or a ventilation image is generated based on voxel intensity variation over time of the time sequence of spatially aligned images.

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.

A system is provided for training a neural network model. The system comprises one or more image acquisition devices, configured to obtain volumetric image data; and one or more processors, configured to: generate images of a plurality of views, which comprise a global complete view and one or more masked views; generate, by a Global-Local Masked AutoEncoder (GL-MAE) encoder system, representations corresponding to the global complete view and the one or more masked views; generating one or more reconstructed images corresponding to the one or more masked views; evaluating in a shared representation space, consistency between the representations of the global complete view and the one or more masked views; computing one or more losses based on the one or more reconstructed images and the results from the consistency evaluation; and updating one or more parameters in the neural network model based on the one or more losses.

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.

A method 100 is disclosed for training an unsupervised Machine Learning (ML) model to reduce noise and aliasing artefacts in a reconstructed 4D medical image of a patient. The method comprises obtaining a projection set comprising a plurality of 2D projections of a training patient volume 110, wherein the 2D projections represent a plurality of phases of a respiratory cycle of the training patient and comprise measurement noise. The method further comprises repeating, for a plurality of iterations, the steps of selecting two non-empty, mutually disjoint subsets that form a partition of the projection set, wherein each of the two selected subsets contains 2D projections that are respiratory uncorrelated 120 for each of the two selected subsets, using a linear reconstruction algorithm to reconstruct a volumetric image of the training patient from the 2D projections contained in the subset 130, and adding the reconstructed volumetric images of the patient to a training data set as an input volume and corresponding target output volume training pair 140. The method further comprises using training pairs from the training data set to update values of the trainable parameters of the ML model 170.

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.

An imaging element may include volume Bragg grating (VBG) within a material, where the material has an input face, and where the VBG is formed as a periodic distribution of curved surfaces of refractive index variation with chirped periodicity in a volume of the material. The periodic distribution of curved surfaces of refractive index variation forming the VBG may be selected to image different object planes oriented parallel to the input face onto different lateral locations of a single image plane with different wavelengths via the Bragg diffraction.