The present disclosure is directed to systems and/or methods that may be used for determining scene information (for example, 3D scene information) using data obtained at least in part from a camera array. Certain embodiments may be used to create scene measurements of depth (and the probability of accuracy of that depth) using an array of cameras. One purpose of certain embodiments may be to determine the depths of elements of a scene, where the scene is observed from a camera array that may be moving through the scene. Certain embodiments may be used to determine open navigable space and to calculate the trajectories of objects that may be occupying portions of that space. In certain embodiments, the scene information may be used to generate a virtual space of voxels where the method then determines the occupancy of the voxel space by comparing a variety of measurements, including spectral response.

A system for forming an object having a three-dimensional structure, the system comprising: a container for providing a photo-curable material to be polymerized; a rotatable stage for supporting the container; a light source for providing light rays having at least one light pattern to be guided into the container via an optical assembly; a processing unit for determining the light source's degree of non-telecentricity, and determining an optimally pre-distorted set of the at least one light pattern based on at least the photo-curable material's refractive index; correcting at least one distortion of the light rays caused by refraction at the container interface and/or correcting at least one distortion of the light rays caused by non-telecentricity; and whereby the correction of the at least one distortion of the light rays is performed without altering the calibration of the optical assembly.

Examples described herein provide a method that includes creating two-dimensional (2D) slices from a plurality of computed tomography (CT) voxel data sets. The method further includes adding artificial noise to the 2D slices to generate artificially noisy 2D slices. The method further includes creating patches from the 2D slices and the artificially noisy 2D slices. The method further includes training an autoencoder using the patches.

A system for processing breast tissue images includes an image processing computer and a user interface operatively coupled to the image processing computer, wherein the image processing computer is configured to obtain image data of breast tissue, processing the image data to generate a set of reconstructed image slices, the reconstructed image slices collectively depicting the breast tissue, process respective subsets of the reconstructed image slices to generate a set of image slabs, each image slab comprising a synthesized 2D image of a portion of the breast tissue obtained from a respective subset of the set of reconstructed image slices.

The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

A tomographic imaging system including an extended source antenna, which produces a wavefield scattered by the internal structure of an object. A processor recursively reconstructs the internal structure by processing a current image of the internal structure of the object with a neural network operator trained to synthesize measurements of a point-source antenna corresponding to a wavefield scattered by the current image of the internal structure of the object, processing the synthesized measurements of the point-source antenna with a calibration neural network to estimate measurements of the extended-source antenna, and updating the current image of the internal structure of the object based on a difference between the measurements of the extended-source antenna and the estimation of the measurements produced by the calibration neural network.

Images are reconstructed from data acquired using an ultra-fast-pitch acquisition with a CT system. As an example, an ultra-fast-pitch acquisition mode in single-source helical CT (1.5) can be used to acquire data. A trained machine learning algorithm, such as a neural network, is used to reconstruct images in which artifacts associated with insufficient data acquired in the ultra-fast-pitch mode are reduced. An example neural network can include customized functional modules using both local and non-local operators, as well as the z-coordinate of each image, to effectively suppress the location- and structure-dependent artifacts induced by the ultra-fast-pitch mode. The machine learning algorithm can be trained using a customized loss function that involves image-gradient-correlation loss and feature reconstruction loss.

A computer-implemented method includes where at least one projection mapping pair of an object under examination by a medical biplane imaging device is acquired. The at least one projection mapping pair contains a first and a second projection mapping of the object under examination, that map the object under examination simultaneously in a first and a second detection plane. The first and second detection planes are arranged non-parallel to one another. A correction model for the correction of an artifact and/or a movement is determined. The artifact or the movement is mapped simultaneously in the at least one first and the at least one second projection mapping. The at least one projection mapping pair specifies a consistency condition for the determination of the correction model. The result dataset is reconstructed at least from the at least one first projection mapping and on the basis of the correction model.

A method for generating a skeleton in an image of a cavity of an organ of a body includes receiving a map of the cavity, the map including surface voxels and interior voxels. A subset of the interior voxels is generated, of candidate locations to be on the skeleton. The subset is pruned by removing outlier candidate locations. Using a geometrical model including a statistical model, the candidate locations remaining in the pruned subset are spatially compressed. The compressed candidate locations are connected to produce one or more centerlines of the skeleton. At least the skeleton is displayed to user.

A self-adaptive motion artifact detection method, and a three-dimensional reconstruction method using the same are provided. The self-adaptive motion artifact detection method includes: acquiring a multi-view rearranged image, decoupling the multi-view rearranged image to obtain a scanning sub-image sequence; dividing each scanning sub-image in the scanning sub-image sequence into blocks; and determining a reference scanning sub-image, and sequentially performing motion artifact detection on individual sub-blocks in the reference scanning sub-image and the sub-blocks in the remaining scanning sub-images.