Abstract: A computer-implemented method for providing medical imaging workflow support data, comprises: receiving natural language data, the natural language data including clinical information; generating structured information by applying a large language model to the natural language data, the structured information including the clinical information in a structured format of at least one input node of an inference graph; calculating the medical imaging workflow support data by applying at least one rule of the inference graph to the structured information; and providing the medical imaging workflow support data.

Abstract: For imaging-based characterization of a perfusion defect in a vessel structure for blood supply of an organ of a patient, medical imaging data is received, wherein the medical imaging data includes energy resolved CT imaging data. A blood stream obstructing object in the vessel structure is detected based on the medical imaging data. A perfusion defect score for a target region of the at least one organ, whose blood perfusion is potentially affected by the obstructing object, is determined depending on the energy resolved CT imaging data.

Abstract: A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.

Abstract: A method for processing a three-dimensional model of a vessel structure comprises: providing the model, the model including a topological graph representation of the vessel structure and first courses of centerlines of vessels; receiving user input data describing at least one corrected course of a centerline; determining, from the user input data and the model, an affected section of the chosen centerline, at least one non-affected section of the chosen centerline, and, for each branch-off along the affected section, a modifiable section connected to the affected section, and a non-modifiable section outside the affected section; determining a deformation field from the affected section, the non-affected section, the non-modifiable section, and the corrected course, such that the deformation field maps the first course of the chosen centerline to the corrected course without changing non-modifiable sections; and applying the deformation field to the affected section and all modifiable sections.

Abstract: A method comprises: retrieving, from a storage, a source data set chosen from at least one prior imaging result data set, each prior imaging result data set including a prior image showing at least part of an acquisition region and annotation information, wherein the annotation information includes location information describing a location of a respective feature of interest in a second coordinate system of the prior image; registering the prior image and the preparation image to obtain registration information including a transformation from the second coordinate system to the first coordinate system; compiling an output image including the preparation image and at least one visual representation, which is positioned correctly according to the location information of the source data set for at least one direction, of all features of interest within a display region comprising the acquisition region; and outputting the output image at a display device.

Abstract: One or more example embodiments of the invention relates in one aspect to a computer-implemented method for providing a stroke information. The method includes receiving examination data, the examination data comprising computed tomography imaging data of an examination area of a patient, the examination area of the patient comprising at least one brain region, the at least one brain region being affected by a stroke; adjusting a causal model based on the computed tomography imaging data to obtain an adjusted causal model, wherein the adjusted causal model models a first variable as a first cause for an appearance of the examination area of the patient; receiving a first value for the first variable; generating the stroke information based on the adjusted causal model and the first value for the first variable; and providing the stroke information.

Abstract: A computer implemented method maps a three-dimensional branched tubular structure depicted in a three-dimensional image data set into at least one two-dimensional image plane.

Abstract: A method for generating augmented camera image data for guiding a needle intervention, comprises: receiving medical image data and camera image data of an examination portion of a patient; determining information concerning a planned insertion pose of the needle based on the medical image data, the information including coordinates assigned to the planned insertion pose of the needle in a coordinate system of the medical image data; determining coordinates assigned to the planned insertion pose of the needle in the camera coordinate system of the camera by transforming the coordinates assigned to the planned insertion pose from the medical image data coordinate system into the camera coordinate system; and generating augmented camera image data based on the camera image data and the coordinates in the camera coordinate system.

Abstract: One or more example embodiments of the present invention relates in one aspect to a computer-implemented method includes receiving 2D topogram data of a patient; generating 2D topogram annotation data by applying a machine learning algorithm for topogram analysis onto the 2D topogram data; generating the medical imaging decision support data based on the 2D topogram annotation data; and providing the medical imaging decision support data.

Abstract: A computer-implemented method for evaluating a three-dimensional angiography dataset of a blood vessel tree of a patient, comprises determining a variant information describing a belonging to at least one anatomical variant class of a plurality of anatomical variant classes relating to anatomical variants of the blood vessel tree based on a comparison of angiography information of the angiography dataset to reference information describing at least one of the anatomical variant classes.

Abstract: A computer-implemented method for the further training of an artificial-intelligence evaluation algorithm that has already been trained based upon basic training data, wherein the evaluation algorithm ascertains output data describing evaluation results from input data comprising image data recorded with a respective medical imaging facility. In an embodiment, the method includes ascertaining at least one additional training data set containing training input data and training output data assigned thereto; and training the evaluation algorithm using the at least one additional training data set. The additional training data set is ascertained from the input data used during a clinical examination process with a medical imaging facility, which the already-trained evaluation algorithm was used, and output data of the already-trained evaluation algorithm that has been at least partially correctively modified by the user.

Abstract: One or more example embodiments relates to a system for providing an automatic segmentation for non-contrast computed tomography imaging data, the system comprising an input data interface configured to obtain imaging data comprising at least non-contrast computed tomography imaging data; a segmentation module configured to implement a segmentation artificial intelligence model, the segmentation artificial intelligence model configured to generate a segmentation for the obtained non-contrast computed tomography imaging data, the segmentation artificial intelligence model being trained at least with imaging data based on segmented contrast computed tomography imaging data; and an output data interface configured to output the generated segmentation.

Abstract: A computer-implemented method and system are for parametrizing a function including a processing algorithm and a representation generator, the representation generator being designed to generate at least one representation. In an embodiment, the method includes using an optimization algorithm to determine the processing algorithm and the at least one representation parametrization. The optimization algorithm optimizes a measure for the performance of the processing algorithm when operating on a set of training representations generated by applying the representation generator to training medical image datasets, by varying on the one hand the content of the at least one representation parametrization and/or the number of used representation parametrizations and on the other hand the processing algorithm and the algorithm parameters.

Abstract: One or more example embodiments relates to a computer-implemented method for providing medical imaging decision support data, the method comprising receiving natural language data, the natural language data comprising patient-specific clinical information; generating structured information by applying a large language model to the natural language data, the structured information comprising the patient-specific clinical information in a structured format; calculating the medical imaging decision support data based on the structured information; and providing the medical imaging decision support data.

Abstract: A method, preferably a computer implemented method, and system are for providing output data associated with a medical imaging dataset of a patient, the medical imaging dataset including image data of a region of an anatomy of a patient including a plurality of coronary arteries. The output data is a significance score associated with a medical imaging data set of a patient. The method, in the most general terms, includes receiving input data, e.g. via a first interface, generating output data by applying algorithmic operations to the input data, and providing the output data, e.g. via a second interface.

Abstract: A method is for providing a classification explanation. The method includes receiving an acquired medical image corresponding to an examination volume; and receiving or determining an actual classification value. The actual classification value classifies an anatomical structure within the acquired medical image. The method further includes receiving or determining a first classification value being different from the actual classification value, the first classification value being a potential classification value of the anatomical structure within the acquired medical image; applying a first generative function to the acquired medical image to generate a first synthetic image, the first generative function being based on the first classification value, and being configured to modify the anatomical structure to exhibit the first classification value; and providing a visualization based on the acquired medical image and the first synthetic image.

Abstract: A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.

Abstract: A computer-implemented method comprises: performing an interactive segmentation process to determine a segmentation of a target volume depicted by a volumetric imaging dataset, the interactive segmentation process including multiple iterations. Each iteration of the interactive segmentation process includes: determining, using a neural network algorithm, a respective estimate of the segmentation; and obtaining, from a user interface, one or more localized user inputs correcting or ascertaining the respective estimate of the segmentation. The neural network algorithm includes multiple inputs, wherein the multiple inputs include an estimate of the segmentation determined in a preceding iteration of the multiple iterations, an encoding of the one or more localized user inputs obtained in the preceding iteration, and the volumetric imaging dataset.

Abstract: A computer-implemented method is for providing a clinical information. In an embodiment, the computer-implemented method includes receiving input data including a graph representation of a plurality of disease lesions of a patient; applying a trained function to the input data to generate the clinical information, the trained function being based on a graph machine learning model; and providing the clinical information, the clinical information including at least one information for prediction of the disease progression, survival or therapy response of the patient.

Abstract: A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.