Systems and methods are provided for registering images. The systems and methods perform operations comprising: receiving, at a first time point in a given radiation session, a first imaging slice corresponding to a first plane; encoding the first imaging slice to a lower dimensional representation; applying a trained machine learning model to the encoded first imaging slice to estimate an encoded version of a second imaging slice corresponding to a second plane at the first time point to provide a pair of imaging slices for the first time point; simultaneously spatially registering the pair of imaging slices to a volumetric image, received prior to the given radiation session, comprising a time-varying object to calculate displacement of the object; and generating an updated therapy protocol to control delivery of a therapy beam based on the calculated displacement of the object.

A system and method for planning surgical procedure including importing CT image data of a patient; generating a 3D reconstruction from the CT image data; presenting a slice of the 3D reconstruction; selecting a target anatomical feature from the slice of the 3D reconstruction; setting a treatment zone including presenting at least one slice of the 3D reconstruction including the target anatomical feature, and presenting a treatment zone marker defining a location and a size of the treatment zone on the presented at least one slice of the 3D reconstruction; setting an access route to the treatment zone; and presenting a three-dimensional model including the treatment zone and the access route.

A method for visualizing a tubular object from a set of volumetric data may include the steps of: determining a viewing direction for the tubular object; selecting a constraint subset of the tubular object within the volumetric data; defining a cut-surface through the volumetric data and including the constraint subset of the tubular object within the volumetric data; and rendering an image based upon the determined viewing direction and the volumetric data of the tubular object along the intersection of the volumetric data and the defined cut-surface. Additionally or alternatively, the method may identify a plurality of bifurcations in the tubular object; assign a weighting factor to each identified bifurcation; determine a bifurcation normal vector associated with each bifurcation; determine a weighted average of the bifurcation normal vectors; and render an image of the volumetric data from a perspective parallel to the weighted average of the bifurcation normal vectors.

An AI system may be configured to combine a marked area of interest (“AOI”) in a medical image (such as from a user clicking an area of a displayed medical image) with natural language understanding (“NLU”) of a description of the medical image (such as provided by the user via voice input) to determine a more precise region for the AOI. This facilitates more accurate and simplified reporting by using the more precise AOI to determine where to automatically place the description in the report and/or to determine how to best supplement the description with automatically determined anatomical information.

The present invention relates to a method for reconstructing an image and an apparatus using the same Particularly, according to the method of the present invention, when a series of first slice images of a subject are inputted in a computing device, the computing device generates, from the first slice images, second slice images having a second slice thickness different from a first thickness, which is the slice thickness of the first slice image, and provides the generated second slice images.

In order to perform denoising on a three-dimensional (3D) spherical measurement of light (such as spherical irradiance probe information or the results of a 3D gonioreflectometry capture), the 3D spherical measurement of light is converted to a two-dimensional (2D) measurement by creating multiple copies of the 3D spherical measurement of light, determining a two-dimensional sub-domain (e.g., a rectangular sub-domain) for each of the multiple copies, and stitching the plurality of two-dimensional sub-domains together in a toroidal configuration. Denoising may then be performed on this 2D measurement via a machine learning implementation or other means. This may result in more accurate 3D spherical light probes that require fewer light measurement samples to generate accurate light measurements.

Systems for and methods for generating precise structure reconstruction using slice and view images, are disclosed. An example method comprises, obtaining a slice and view images of a sample that depicts a 3D fiducial and cross-sections of a structure in the sample. The 3D fiducial is configured such that when a layer of material having a uniform thickness is removed from a surface of the sample that includes the 3D fiducial the cross-sectional shape of the 3D fiducial in the new surface is consistent. Relative positions are determined between the 3D fiducial the cross-sections of the structure in individual images. Positional relationships are then determined between the cross-sections of the structure in different images in a common reference frame based on the relative positions.

A system and a computer-implemented method of evaluating a formation. The system includes a plurality of acoustic transmitters and acoustic receivers and a processor. Acoustic data is obtained from the formation using the plurality of acoustic transmitters and acoustic receivers. The acoustic data is projected into a plurality of image planes and a feature in one of the image planes is selected. The plurality image planes are scrolled through in order to determine the three-dimensional structure of the feature. The formation is drilled based on the three-dimensional structure of the feature.

A method for generating clinically valuable analyses and visualizations of 3D volumetric OCT data by combining a plurality of segmentation techniques of common OCT data in three dimensions following pre-processing techniques. Prior to segmentation, the data may be subject to a plurality of separately applied pre-processing techniques.

Systems and methods are provided for registering images. The systems and methods perform operations comprising: receiving, at a first time point during a given radiation session, a first imaging slice comprising an object, the first imaging slice corresponding to a first plane; accessing, at the first time point during the given radiation session, a composite imaging slice corresponding to the first plane, the composite imaging slice being generated using a plurality of imaging slices obtained prior to the first time point; spatially registering the first imaging slice and the composite imaging slice; determining movement of the object using the spatially registered first imaging slice and the composite imaging slice; and generating an updated therapy protocol to control delivery of a therapy beam based on the determined movement.