A method includes, in a processor, receiving example representations of geometrical shapes of a given type of organ. In a training phase, a neural network model is trained using the example representations. In a modeling phase, the trained neural network model is applied to a set of location measurements acquired in an organ of the given type, to produce a three-dimensional model of the organ.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to extract a degree of a disease related to the heart from a medical image; and display, when the degree of the disease related to the heart is high, a first index value related to a blood vessel and calculated from one of blood pressure and a blood flow and configured to display, when the degree of the disease related to the heart is low, wall shear stress serving as a second index value related to the blood vessel, as information related to the blood flow of the blood vessel and calculated on the basis of the medical image.

In one embodiment, a method for generating a three-dimensional (3D) anatomical map, including applying a trained artificial neural network to (a) a set of two-dimensional (2D) fluoroscopic images of a body part of a living subject, and (b) respective first 3D coordinates of the set of 2D fluoroscopic images, yielding second 3D coordinates of the 3D anatomical map, and rendering to a display the 3D anatomical map responsively to the second 3D coordinates.

A marker-coordinate detecting unit detects coordinates of a stent marker on a new image when the new image is stored in an image-data storage unit; and then a correction-image creating unit creates a correction image from the new image through, for example, image transformation processing, so as to match up the detected coordinates with reference coordinates that are coordinates of the stent marker already detected by the marker-coordinate detecting unit in a first frame. An image post-processing unit then creates an image for display by performing post-processing on the correction image created by the correction-image creating unit, the post-processing including high-frequency noise reduction filtering-processing, low-frequency component removal filtering-processing, and logarithmic-image creating processing; and then a system control unit performs control of displaying a moving image of an enlarged image of a set region that is set in the image for display, together with an original image.

An imaging steering apparatus includes sensors and an imaging processor configured for: acquiring, via multiple ones of the sensors and from a current position (322), and current orientation (324), an image of an object of interest; based on a model, segmenting the acquired image; and determining, based on a result of the segmenting, a target position (318), and target orientation (320), with the target position and/or target orientation differing correspondingly from the current position and/or current orientation. An electronic steering parameter effective toward improving the current field of view may be computed, and a user may be provided instructional feedback (144) in navigating an imaging probe toward the improving. A robot can be configured for, automatically and without need for user intervention, imparting force (142) to the probe to move it responsive to the determination.

Systems and methods for performing motion artifact correction in medical images. One method includes receiving, with an electronic processor, a medical image associated with a patient, the medical image including at least one motion artifact. The method also includes applying, with the electronic processor, a model developed using machine learning to the medical image for correcting motion artifacts, the model including at least one of a spatial transformer network and an attention mechanism network. The method also includes generating, with the electronic processor, a new version of the medical image, where the new version of the medical image at least partially corrects the at least one motion artifact.

A method of segmentation of a three-dimensional image for generating a model of a myocardial wall includes recording a three-dimensional image of a wall of the myocardium, the wall delimiting at least one cavity of the heart; segmenting a continuous part of the wall into at least a first volume having a thickness less than a first predefined thickness threshold of between 0 and 5 mm and a second volume of a continuous part of the wall having a thickness greater than the first threshold; generating a model of the wall of the myocardium, where the continuous part of the wall of the myocardium is modelled according to at least two volumes that continue each other.

The disclosure relates to a method and a tracking system for tracking a medical object. Herein, image data obtained by an imaging method and a predetermined target position is acquired for the medical object. The image data is used to detect the medical object automatically by an image processing algorithm and track the position thereof in a time-resolved manner. Furthermore, it is furthermore indicated when, or that, the detected medical object has reached the target position. A plurality of the detected positions of the medical object and associated detection times are stored in a database.

A method and apparatus for generating a T1 or T2 map for a three-dimensional (3D) image volume of a subject. The method includes acquiring first, second, and third 3D images of the image volume of the subject. Signal evolutions of voxels through the first to third 3D images by comparing voxel intensity levels of corresponding voxel locations in the first, second, and third 3D images. A simulation dictionary representing the signal evolutions for a number of different tissue parameter combinations is obtained. The T1 or T2 map is generated by comparing the determined signal evolutions to entries in the dictionary and by finding, for each of the determined signal evolutions, the entry in the dictionary that best matches the determined signal evolution.