Siemens Healthcare Gmbh

Siemens Healthineers Ag

Martin Berger

Marcus Pfister

Thomas Redel

Tiziano Passerini

Mehmet Akif Gulsun

Puneet Sharma

Günter Lauritsch

Dominik Neumann

Anton Nekovar

Alexander Preuhs

Sebastian Bauer

Serkan Cimen

Martin Von Roden

Angiography angles are determined. Patient information and target vessel information are obtained, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged. At least one angiography angle is determined based on the patient information and the target vessel information. Angiograms obtained using the at least one angiography angle are analyzed to determine a vessel coverage of the target vessel and based on the vessel coverage determines additional angiography angles.

Method and system for improved vessel coverage from angiography

For particularly good communication even with difficult patients, a monitoring system for monitoring a patient, such as during a medical diagnostic or therapeutic procedure, is provided. The monitoring system includes a voice cloning device having a voice generator. The voice cloning device is configured to replace a natural voice of a person with a cloned synthetic voice different from the voice of the person. At least two synthetic voices may be selected. The monitoring system also includes a measuring unit configured to record at least one physiological parameter of the patient, an evaluation unit configured to evaluate the at least one measured physiological parameter of the patient, and an actuation unit configured to actuate the voice cloning device such that a synthetic voice is selected or rejected depending on a result of the evaluation by the evaluation unit.

Monitoring system for monitoring a patient and method for operating the monitoring system

The disclosure relates to a display apparatus for displaying an augmented and/or virtual reality. The display apparatus has a sensor unit configured to capture a user input. The display apparatus is further configured to receive a medical planning dataset and receive a medical image dataset having at least one projection mapping. The display apparatus is further configured to generate and display the augmented and/or virtual reality based on the planning dataset and the at least one projection mapping. The display apparatus is further configured to adapt a virtual spatial positioning of the planning dataset relative to the at least one projection mapping in the augmented and/or virtual reality based on the user input. The disclosure further relates to a system, a method for registering a planning dataset with an image dataset, and a computer program product.

Adapting an augmented and/or virtual reality

According to a computer-implemented method for the automatic localization of a structure, an individual image which maps an object with the structure is displayed on a display surface. A gaze movement of a user is captured by a gaze capture system and, on the basis thereof, a gaze capture signal is generated. By a computer unit, dependent upon the gaze capture signal, at least one image point of the individual image is identified, which at least partially maps the structure.

Automatic localization of a structure

Systems and methods for determining a 3D centerline of a vessel are provided. A current state observation of an artificial agent is determined based on one or more image view sets, each including 2D medical images of a vessel, a current position of the artificial agent in the 2D medical images, and a start position and a target position in the 2D medical images. Policy values are calculated for a plurality of actions for moving the artificial agent in 3D based on the current state observation using a trained machine learning model. The artificial agent is moved according to a particular action based on the policy values. The steps of determining, calculating, and moving are repeated for a plurality of iterations to move the artificial agent along a 3D path between the start position and the target position. The 3D centerline of the vessel is determined as the 3D path.

3D vessel centerline reconstruction from 2D medical images

A method and system are provided for at least symbolically reconstructing a reconstruction data set of at least one vessel segment in a vessel tree of a patient. Input data for the reconstruction comprises at least two two-dimensional angiographic projection images taken in different acquisition geometries. At least one first angiographic projection image showing the vessel segment is acquired. An evaluation measure is automatically determined for each first angiographic projection image using three-dimensional preliminary information for the vessel segment. The evaluation measure describes the suitability of the at least one angiographic projection image for reconstructing the reconstruction data set. When a quality criterion evaluating the evaluation measure is not fulfilled, at least one additional acquisition geometry is determined using the three-dimensional preliminary information and/or the evaluation measure. In each additional acquisition geometry, at least one second angiographic projection image is acquired. The reconstruction data set is reconstructed from the at least one second angiographic projection image and/or at least one of the at least one first angiographic projection image fulfilling a suitability criterion evaluating the evaluation measure.

Method for reconstructing a reconstruction data set of a vessel segment

Systems and methods are provided for evaluating the complexity of a stenosis or a section of a vessel. At least one image of the stenosis or the section of the vessel is provided. A geometrical feature value of the stenosis and/or or the section of the vessel is identified from the at least one image. At least one intensity feature value is determined based on a grey level intensity of the stenosis or the section of the vessel from the at least one image. A complexity value relating to the geometrical complexity of the stenosis or the section of the vessel is calculated as a function of the at least one geometrical feature value and the at least one intensity feature value of the stenosis or the section of the vessel.

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Erlangen, Germany

Martin Berger