A system for determining an optimal hardness of a patient interface device includes a fit score determination unit structured to receive a 3-D model of the patient interface device and a 3-D model of a patient's face and to determine a fit score between the patient interface device and the patient's face based on the 3-D model of the patient interface device and the 3-D model of the patient's face, and a hardness determination unit structured to determine a hardness value of the patient interface device based on the determined fit score.

A computer-implemented method for voxelizing a 3D structural medical image of a human's brain. The method including obtaining a 3D structural medical image of the human's brain, including a reference frame, generating a voxelized 3D structural medical image, obtaining parameters of at least one EEG electrode sensor and, for each EEG electrode sensor: a localization in the voxelized 3D structural medical image's reference frame, and a sensor detection distance, obtaining a regular 3D grid of voxels, and for each voxel of the 3D grid, iteratively subdividing the voxel while the distance between the voxel and the localization of any electrode sensor is smaller than or equal to the sensor detection distance and while a size of the voxel is greater than a predetermined length, each subdivided voxel joining a finite number of voxels of the voxelized 3D structural medical image.

An image processing device is an image processing device configured to cause a display to display three-dimensional data as a three-dimensional image, the three-dimensional data representing a biological tissue. The image processing device includes: a control unit configured to adjust a color tone of each pixel of the three-dimensional image according to a dimension of the biological tissue in a linear direction from a viewpoint when the three-dimensional image is displayed on the display.

An image processing device is an image processing device configured to cause a display to display, as a three-dimensional image, three-dimensional data representing a biological tissue having a longitudinal lumen. The image processing device includes: a control unit configured to calculate centroid positions of a plurality of cross sections in a lateral direction of the lumen of the biological tissue by using the three-dimensional data, set a pair of planes intersecting at a single line passing through the calculated centroid positions as cutting planes, and form, in the three-dimensional data, an opening exposing the lumen of the biological tissue from a region interposed between the cutting planes in the three-dimensional image.

An image processing device is an image processing device configured to cause a display to display three-dimensional data as a three-dimensional image, the three-dimensional data representing a biological tissue. The image processing device includes: a control unit configured to detect a series of positions of a catheter inserted into the biological tissue from data obtained in time series by a sensor observing the surrounding of a lumen of the biological tissue while moving in the lumen, and to switch a display mode between a first mode for displaying a first figure representing the series of positions in the three-dimensional image, and a second mode for displaying a second figure representing one position among the series of positions in the three-dimensional image.

Methods, systems, devices and apparatuses for generating a high-quality MRI image from under-sampled or corrupted data The image reconstruction system includes a memory. The memory is configured to store multiple samples of biological, physiological, neurological or anatomical data that has missing or corrupted k-space data and a deep learning model or neural network. The image reconstruction system includes a processor coupled to the memory. The processor is configured to obtain the multiple samples. The processor is configured to determine the missing or corrupted k-space data using the multiple samples and the deep learning model or neural network. The processor is configured to reconstruct an MRI image using the determined missing or corrupted k-space data and the multiple samples.

A system and method for generating simulated full limb animations in real time based on sensor and tracking data. A computing environment for receiving and processing tracking data from one or more sensors, for mapping tracking data onto a 3D model having a skeletal hierarchy and a surface topology, and for rendering an avatar for display in virtual reality. A method for animating a full-bodied avatar from tracking data collected from an amputee. A means for determining, predicting, or modulating movements an amputee intends to make with his or her simulated full limb. A modified inverse kinematics method for arbitrarily and artificially overriding a position and orientation of a tracked end effector. Synchronous virtual reality therapeutic activities with predefined movement patterns that may modulate animations.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Interaction Engine. According to various embodiments, Interaction Engine generates within a unified three-dimensional (3D) coordinate space, a virtual 3D medical model positioned according to a model pose. The Interaction Engine receives video data from a plurality of video sources. The Interaction Engine renders a first Augmented Reality (AR) display that includes concurrent display of the virtual 3D medical model and visualization of at least a portion of video data from a first video source. The Interaction Engine renders a second Augmented Reality (AR) display that includes concurrent display of the virtual 3D medical model and visualization of at least a portion of video data from a second video source.

The invention relates to a system (1) for reviewing 3D or 4D medical image data (2), the system (1) comprising (a) a medical review application (MRA) (4) comprising a processing module (6) configured to process a 3D or 4D dataset (2) to generate 3D content (8), and a 2D user interface (16); wherein the 2D user interface (16) is configured to display the 3D content (8) and to allow a user (30) to generate user input (18) commands; (b) an extended reality (XR)-based user interface add-on (XRA) (100); and (c) a data exchange channel (10), the data exchange channel (10) being configured to interface the processing module (6) with the XRA (100); wherein the XRA (100) is configured to interpret and process the 3D content (8) and convert it to XR content displayable to the user (30) in an XR environment (48); wherein the XR environment (48) is configured to allow a user to generate user input (18) events, and the XRA (100) is configured to process the user input (18) events and convert them to user input (18) commands readable by the MRA (4). The invention also relates to an extended reality-based user interface add-on (100), a related method for analysing a 3D or 4D dataset (2), and a related computer program.

A method for diagnostic imaging reconstruction uses a prior image x.sup.pr from a scan of a subject to initialize parameters of a neural network which maps coordinates in image space to corresponding intensity values in the prior image. The parameters are initialized by minimizing an objective function representing a difference between intensity values of the prior image and predicted intensity values output from the neural network. The neural network is then trained using subsampled (sparse) measurements of the subject to learn a neural representation of a reconstructed image. The training includes minimizing an objective function representing a difference between the subsampled measurements and a forward model applied to predicted image intensity values output from the neural network. Image intensity values output from the trained neural network from coordinates in image space input to the trained neural network are computed to produce predicted image intensity values.