The present application relates to a three-dimensional imaging method and apparatus, and a 3D imaging device. The method comprises generating 3D image information by capturing a 3D capture area containing a detected object using a depth camera; extracting a mask of the detected object from the 3D image information; determining an imaging area associated with the detected object based on the mask of the detected object; collecting data from a holographic data collection area containing the detected object by a holographic data collection device, generating holographic data; and performing image reconstruction on the imaging area based on the holographic data.

According to one embodiment, a medical image diagnostic apparatus includes a storage memory, processing circuitry, and a display. The storage memory stores data of a plurality of FFR distribution maps constituting a time series regarding a coronary artery, and data of a plurality of morphological images corresponding to the time series. The processing circuitry converts the plurality of FFR distribution maps into a plurality of corresponding color maps, respectively. The display displays a plurality of superposed images obtained by superposing the plurality of color maps and the plurality of morphological images respectively corresponding in phase to the plurality of color maps. The display restricts display targets for the plurality of color maps based on the plurality of FFR distribution maps or the plurality of morphological images.

An imaging system and reconstruction method are described. The method includes identifying at least one region of interest in a first reconstructed image, generating a region-of-interest orthographic projection image of each region of interest and a background-region orthographic projection image of a background region, obtaining a region-of-interest filtered orthographic projection image of each region of interest and a background-region filtered orthographic projection image, wherein the region-of-interest filtered orthographic projection image is obtained by filtering a current-region-of-interest orthographic projection image using a filter kernel function matched with a current region of interest, and the background-region filtered orthographic projection image is obtained by filtering the background-region orthographic projection image using a filter kernel function matched with the background region, and generating a second reconstructed image based on the region-of-interest filtered orthographic projection image of each region of interest and the background-region filtered orthographic projection image.

A system and method for generating a three-dimensional image of a sample. The system comprises a platform configured to support the sample, a laser source configured to output a laser beam to remove material from the sample, a microscope imaging system, and a controller communicatively coupled to the laser source and the microscope imaging system. The controller is configured to (a) acquire height data of a surface of the sample with the microscope imaging system, (b) generate a mask based on the height data of the surface of the sample, the mask providing a laser cutting pattern for the sample, (c) acquire an image and a height map of the surface of the sample with the microscope imaging system, (d) activate the laser source to generate a laser beam to delayer the sample based on the mask, and repeat steps (a)-(d) to generate a three-dimensional image of the sample.

Systems and methods for performing a medical imaging analysis task using a machine learning based model are provided. One or more input medical images acquired using one or more out-of-distribution image acquisition parameters and having out-of-distribution imaging properties are received. The one or more out-of-distribution image acquisition parameters and the out-of-distribution imaging properties are out-of-distribution with respect to training data on which the machine learning based model is trained. One or more synthesized medical images are generated from the one or more input medical images using a machine learning based generator network. The one or more synthesized medical images are generated for one or more in-distribution image acquisition parameters and have in-distribution imaging properties. The one or more in-distribution image acquisition parameters and the in-distribution imaging properties are in-distribution with respect to the training data on which the machine learning based model is trained. The medical imaging analysis task is performed based on the one or more synthesized medical images using the machine learning based model. Results of the medical imaging analysis task are output.

Systems and methods for image reconstruction of simultaneous multi-slice (SMS) magnetic resonance data using diffusion models. An SMS diffusion plug and play model is based on DDIM and supports fast sampling. The SMS diffusion plug and play model includes data consistency based on a data proximal subproblem that incorporates an SMS imaging model.

A processor is configured to acquire a plurality of CT images derived by reconstructing a plurality of pieces of projection data having different energy levels, which are acquired by imaging a subject at a plurality of timings, at a plurality of predetermined energy levels, derive visibility information related to visibility of a target region included in each of the plurality of CT images, and determine an image interpretation energy level for interpreting the CT image among the plurality of energy levels based on the visibility information.

A CT apparatus in which the x-ray source is coupled to a source robotic arm and the detector is coupled to a detector robotic arm. A motion correction module utilizes a locally linear embedding motion correction algorithm to estimate the geometry-describing parameters associated with the positions of the source and detector and the angle of the detector. These estimates are used to reconstruct the image data and produce corrected images with fewer errors resulting from patient movement, misalignments, and coordination issues in the system.

Described is a system and/or method for displaying the location of a ferromagnetic object in a living organism by using a surgical probe. The surgical probe has a shaft with three-dimensional magnetoresistance sensors located on a distal end configured for insertion into the living organism and three-dimensional magnetoresistance sensors located on a proximal end that stays outside of the living organism. The system comprises a display configured to show the relative location of a detected ferromagnetic object to the tip of the probe in a simulated three-dimensional view on a two-dimensional display.

Methods and systems for 3D image analysis including identifying a region of interest of a blood vessel in a medical image. The methods and systems include identifying a first point disposed on the blood vessel, the first point being a first center point of the blood vessel on a first side of the region of interest and identifying a second point disposed on the blood vessel, the second point being a second center point of the blood vessel on a second side of the region of interest. The methods and systems include generating a curved planar reconstruction (CPR) image of a portion of the blood vessel, the portion extending from the first point to the second point and including the region of interest and determining the diameter of the blood vessel at the region of interest.