From a plurality of medical images in time phases, a target site is extracted from at least one medical image, a reference point is set on each of a target-site side, and a periphery side of the target site which are on across from each other over an outline of the extracted target site, and movement information for the reference points is calculated.

A method of displaying electrophysiology information includes obtaining a three-dimensional medical image of an anatomical region, registering a localization system to the model; localizing an electrophysiology catheter within the anatomical region; displaying a representation of the localization of the electrophysiology catheter on the model; and displaying image slices of the model. The image slices are selected based upon the localization of the electrophysiology catheter. For example, the image slices can pass through a user-selected localization element carried by the electrophysiology catheter. Rigid and/or non-rigid transforms can be used to register the localization system to the model. Electrophysiology data collected by the catheter can be displayed on the model and/or the image slices thereof. The three-dimensional medical image and/or the electrophysiology data can also be time-varying. In embodiments, scalar maps can also be displayed on the model.

The invention relates to a method for determining the position of an object moving within a body, wherein the body is connected to markers, a movement signal is determined based on the measured movement of the markers, images are taken from the object using a camera or detector, wherein the camera or detector is moved with respect to the object, it is determined from which direction or range of angles or segment the most images corresponding to a predefined cycle of the movement signal are taken, and using at least some or all of the images of the segment containing the most images for a specified movement cycle, an image of the object is reconstructed.

A method and system for tracking blood flow within a vessel of a patient are presented. At least one or more medical images of the patient are acquired showing at least one vessel of the patient. A user marks a proximal point and a distal point on a vessel of interest on the medical images. The vessel of interest is tracked and corrections are made to the tracking using a tracking algorithm. A composite image is generated that encodes time to peak contrast agent intensity at each point of the vessel of interest as well as the intensity of the contrast at that time. A graph of time to peak contrast agent intensity versus distance from a proximal point of the vessel of interest is calculated and displayed to a user.

Systems and methods for assessing expansion of an abnormality are provided. A first input medical image of a patient depicting an abnormality at a first time and a second input medical image of the patient depicting the abnormality at a second time are received. The second input medical image is registered with the first input medical image. The abnormality is segmented from 1) the first input medical image to generate a first segmentation map and 2) the registered second input medical image to generate a second segmentation map. The first segmentation map and the second segmentation map are combined to generate a combined map. Features are extracted from the first input medical image and the registered second input medical image are based on the combined map. Expansion of the abnormality is assessed based on the extracted features using a trained machine learning based network. Results of the assessment are output.

Disclosed are techniques for automated analysis and assessments of contrast medium absorption phases in contrast medium based medical images. A target image set includes a plurality of medical images acquired to image a plurality of contrast medium absorption phases. For the images of the target image set, a set of contrast medium absorption phase probabilities are determined corresponding to likelihoods that a given image corresponds to a given contrast medium absorption phase. The determined sets of contrast medium absorption phases are compared against a reference set of contrast medium absorption phases to determine differences to determine a set of matching scores indicative of how closely the contrast medium absorption phases of the target image set align with the plurality of contrast medium absorption phases as compared to the reference set of contrast medium absorption phases.

A method for cardiac computed tomography imaging acquires 4D image volumes using cardiac CT, generates synthetic low noise data from the 4D image volumes for a complete cardiac cycle by exploiting redundancies in the data, uses the generated synthetic low noise data to train a 3D U-Net with 2D+time architecture to produce denoised cardiac CT output from noisy cardiac CT input, and uses the trained 3D U-Net for deep learning based denoising of functional cardiac CT data.

In a method for automatic motion detection in medical image-series, a dataset of a series of images is provided. The images can be of a similar region of interest that are recorded at consecutive points of time. The method can further include localizing a target in the images of the dataset and calculating a position of the target in the images to calculate localization data of the target, and calculating movement data of a movement of the target of temporal adjacent images of the images based on the localization data.

A computer-implemented medical visualization method includes identifying a three-dimensional model of an anatomical item of a particular mammal; automatically identifying an open path in three-dimensional space through the anatomical item; fitting a smooth curve to the open path; and displaying the anatomical item and a visual representation of the smooth curve to a user on a three-dimensional imaging system.

There is provided a medical image processing apparatus which includes a first extraction unit configured to extract coronary arteries depicted in images of a plurality of time phases relating to the heart, and to extract at least one stenosed part depicted in each coronary artery; a calculation unit configured to calculate a pressure gradient of each of the extracted coronary arteries, based on tissue blood flow volumes of the coronary arteries; a second extraction unit configured to extract an ischemic region depicted in the images; and a specifying unit configured to specify a responsible blood vessel of the ischemic region by referring to a dominance map, in which each of the extracted coronary arteries and a dominance territory are associated, for the extracted ischemic region, and to specify a responsible stenosis, based on the pressure gradient corresponding to a stenosed part in the specified responsible blood vessel.