Abstract: A computer-implemented method for scattered radiation correction, comprising the steps: obtaining projection images, optimizing a measure of quality by variation of correction parameters depending on the projection images, wherein a determination algorithm, which serves to determine the measure of quality, is an algorithm trained by machine learning and processes as its input data a reconstructed three-dimensional image dataset or processing data that is chosen from the image dataset, wherein the image dataset is based on corrected projection images, wherein a respective corrected projection image results from application of a correction algorithm, wherein the correction algorithm is parameterized by the correction parameters, provision of radiation scatter-corrected projection images and/or of a radiation scatter-corrected reconstructed three-dimensional image dataset.

Abstract: For monitoring a room with a medical technology apparatus, a light projection that defines an areal region in the room is generated with light of a predetermined wavelength characteristic. Using a large number of optical detectors that are each mounted in the areal region and are configured for detecting light having the wavelength characteristic, at least one detector signal is generated. Dependent upon the at least one detector signal, a coverage of at least a portion of the large number of optical detectors by an object is recognized. Dependent upon the recognized coverage, a safety and/or warning measure is initiated.

Abstract: To assess a risk of rupture of a hollow organ, at least one first image is obtained that maps a first expansion state of a balloon catheter disposed in the hollow organ. Based on the at least one first image, a balloon size and an internal balloon pressure of the balloon catheter in the first expansion state are determined. At least one second image is obtained that maps a second expansion state of the balloon catheter disposed in the hollow organ. Based on the at least one second image, the balloon size and the internal balloon pressure in the second expansion state are determined. Based on the internal balloon pressures in the first and second expansion states and the balloon sizes in the first and second expansion states, information is generated for assessing a risk of rupture of the hollow organ.

Abstract: A graphical display is adjusted. A dataset having a map and/or a model of a plurality of anatomical and/or medical objects of an object under examination is acquired. A graphical display of the dataset is visualized by a display. Based on a capturing signal, at least one of the plurality of anatomical and/or medical objects being viewed by a viewer of the graphical display is identified. The capturing signal is provided by a capturing unit embodied to capture a partial area of the graphical display currently being viewed by a viewer and to provide the capturing signal in dependence on the captured partial area. The graphical display of the at least one identified object is adjusted by adjusting a display parameter of the graphical display.

Abstract: A method for collision avoidance when positioning a medical imaging device and a patient positioning apparatus includes capturing a representation of an examination object, identifying an examination region based on representation, and identifying an initial acquisition trajectory around an initial isocenter, which enables a 3D reconstruction of the examination region. The imaging device and/or the patient positioning apparatus is positioned such that an isocenter of the medical imaging device coincides with the initial isocenter. A further acquisition trajectory is identified around one further isocenter, which enables a collision-free positioning of the imaging device and a 3D reconstruction of the examination region. The further isocenter specifies a permissible range for relative positionings of the imaging device and the patient positioning apparatus. The imaging device and/or the patient positioning apparatus is repositioned. Repositioning is limited to a permissible range of relative positionings.

Abstract: A computer-implemented method and system for providing a trained artificial intelligence determination algorithm for correcting x-ray image data with regard to noise effects, for example for scattered radiation correction of the x-ray image data. The determination algorithm, from input data comprising a recorded image dataset, determines a noise effect dataset describing the noise effects to be used for correction of the x-ray image dataset. A statistical physics model parameterized by model parameters is used to describe the noise effects. The model parameters are able to be determined at least in part using the determination algorithm including receiving of training datasets that include x-ray image sub datasets with assigned, known noise effect sub datasets and/or with at least one assigned, noise-free reference image sub dataset, training the determination algorithm using the training datasets, and providing the trained determination algorithm.

Abstract: In order to reduce a computing effort for correcting initial images from an imaging apparatus, a scatter model is used to simulate scattered-radiation images, and corresponding initial images are selected from the original initial images. These are used to train a neural network. The neural network may be used to calculate scattered-radiation single images that may be used to correct each individual initial image into a corrected initial image.

Abstract: A method for operating an X-ray facility for recording a three-dimensional (3D) image data set of a target area of a patient is provided. A recording arrangement including an X-ray detector and an X-ray source may be rotated about an axis of rotation for recording two-dimensional projection images based on the image data set. A model instance of a parameterizable patient model that is patient-specific and 3D is determined. Target area information describing the target area is determined in the model instance from default information. At least two at least partially different partial recording areas of the target area are determined from the target area information. The partial recording areas cover the target area along the axis of rotation. One projection image set is recorded for each of the partial recording areas, and the image data set is reconstructed from the projection image sets.

Abstract: A method for providing a collision model includes detecting an item of patient information that has an item of information on a spatial placement of an entry point of a medical object into an object undergoing examination. The item of patient information is registered with a movable medical device. The method also includes detecting an item of object information that has an item of information on a geometry and spatial position of a manipulator for moving the medical object. The manipulator includes a motion device and/or a member of technical medical personnel. A relative placement of the manipulator in relation to the object undergoing examination is determined based on the item of patient information and the item of object information. The collision model is provided for the purpose of moving the medical device based on the relative placement and the item of object information.

Abstract: A monitoring system for monitoring at least one target object in a medical environment.

Abstract: A method for providing at least one procedure parameter includes acquiring at least one intraprocedural projection map that maps a hollow organ of an examination object with at least one medical object positioned in the hollow organ. A trained function is applied to the at least one intraprocedural projection map as input data. At least one parameter of the trained function is adjusted based on a simulation of a virtual positioning of at least one medical training object in a training hollow organ and of hemodynamics in the training hollow organ that are influenced by the at least one medical training object. The at least one procedure parameter is provided as output data of the trained function. The at least one procedure parameter includes a movement specification for the at least one medical object.

Abstract: A method for monitoring a medical intervention taking place in an operating room or examination room, or a medical examination on a patient by medical personnel includes acquiring sensor data from at least one acoustic sensor, of at least one individual among the medical personnel during the medical intervention or the medical examination. The sensor data is evaluated as regards recognition of stress and/or negative emotions of the at least one individual. When stress and/or negative emotions are recognized, at least one cause of the stress and/or of the negative emotions is determined using the sensor data and/or further optical, acoustic, haptic, digital, and/or other data acquired during the medical intervention or the medical examination. Measures are triggered to remediate the determined cause and/or to reduce the stress and/or the negative emotions of the at least one individual.

Abstract: In accordance with a method for dose estimation for the irradiation of an object, a model with a total number of spatial elements is provided on a memory element. For each spatial element, the model specifies a material composition of the object. A neighborhood material composition is determined for a neighborhood of spatial elements depending on the model by a computing unit. A radiation dose for the neighborhood with regard to an ionizing radiation is determined with aid of a simulation depending on the neighborhood material composition. A dose distribution for the object with regard to the ionizing radiation is determined based on the radiation dose for the neighborhood.

Abstract: A computer-implemented method and system for providing a trained artificial intelligence determination algorithm for correcting x-ray image data with regard to noise effects, for example for scattered radiation correction of the x-ray image data. The determination algorithm, from input data comprising a recorded image dataset, determines a noise effect dataset describing the noise effects to be used for correction of the x-ray image dataset. A statistical physics model parameterized by model parameters is used to describe the noise effects. The model parameters are able to be determined at least in part using the determination algorithm including receiving of training datasets that include x-ray image sub datasets with assigned, known noise effect sub datasets and/or with at least one assigned, noise-free reference image sub dataset, training the determination algorithm using the training datasets, and providing the trained determination algorithm.