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 for controlling a medical imaging installation, comprises: capturing patient-specific indication data via an interface unit; capturing at least one user-specific and/or facility-specific preference parameter via an interface unit; automatically determining, via an arithmetic unit, a control data record on the basis of the indication data and the preference parameter; and controlling, via the arithmetic unit, the medical imaging installation using the control data record.

Abstract: Techniques for determining at least one characteristic of adipose tissue included in an anatomical structure are provided. The at least one characteristic of the adipose tissue is determined based on one or more segmented CT images using a trained neural network. For example, the at least one characteristic of the adipose tissue may be determined by inputting the one or more segmented CT images into the trained neural network. The one or more segmented CT images are obtained by segmenting each one of one or more CT images depicting the anatomical structure including the adipose tissue to determine a contour of the adipose tissue.

Abstract: In particular, one or more example embodiments relates to a (e.g. computer-implemented) method for detecting free intra-abdominal air. The method comprises—receiving input data, said input data comprising a medical imaging data set of an abdominal region of a patient, e.g. via a first interface; applying a trained function, wherein the output data is generated, providing the output data e.g. via a second interface.

Abstract: A method is for supporting clinical decisions for a diagnosis or therapy of a patient using a medical imaging system. An embodiment of the method includes receiving a procedure order; based on the received procedure order, automatically identifying a clinical context of the ordered procedure; generating preliminary imaging data of at least a part of the patients anatomy; generating feature extraction data based on the identified clinical context and on the preliminary imaging data; extracting at least one clinical context specific feature using the generated feature extraction data; annotating the at least one extracted clinical context specific feature to obtain at least one annotated extracted feature; and selecting, for the identified clinical context, a similar case data set from a reference database of case data sets based on the at least one annotated extracted feature.

Abstract: A method is for controlling a medical imaging system. The method includes providing a data record for a patient, the data record including indication data; mapping at least the indication data into an ontology; providing a control data library including control data records for at least one medical imaging system; automatically determining a control data record from the control data library as a function of the at least indication data mapped into the ontology, via at least one of an inference and numerical modeling; and controlling the medical imaging system with the control data record automatically determined. Furthermore, a related control facility and a medical imaging system are also disclosed.

Abstract: An embodiment relates to a computer-implemented method for generating a combined tissue-vessel representation. The method includes receiving imaging data of a tissue; receiving imaging data of a vessel; generating a tissue representation based on the imaging data of the tissue; generating a vessel representation based on the imaging data of the vessel; and generating a combined tissue-vessel representation based on the vessel representation and the tissue representation, the vessel representation being overlaid over the tissue representation.

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: The method comprises receiving an image sequence of the subject from the camera during the medical imaging scan; receiving at least one of the current position or velocity of the patient table during the medical imaging scan; performing a motion tracking analysis of the image sequence to extract a motion model, wherein at least one of the motion tracking analysis or the motion model is tailored to the body region of interest and takes into account the at least one of the current patient table position or velocity; and analysing the motion model to detect subject motion and, if the detected motion is above a threshold, at least one of adapting the medical imaging examination or issuing an alert.

Abstract: A trained ML algorithm may be configured to process medical imaging data to generate a prediction of at least one diagnosis of a patient based on the medical imaging data. The prediction of the at least one diagnosis of the patient is compared with a validated label of the at least one diagnosis of the patient and the performance of the trained ML algorithm is determined based on the comparison. The validated label of the at least one diagnosis of the patient is obtained by parsing a validated radiology report of the patient and the medical imaging data is associated with the validated radiology report. If the performance of the trained ML algorithm is lower than a threshold, an update of parameters of the trained ML algorithm may be triggered based on the validated label.

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: In one embodiment, a computer-implemented method provides a prediction of a medical target variable. The computer-implemented method includes receiving medical imaging data of an examination area, the examination area including a plurality of lesions of an anatomical structure, wherein each lesion of the plurality of lesions of the anatomical structure spaced apart from any other lesion of the plurality of lesions of the anatomical structure; calculating a spread parameter based on the medical imaging data, the spread parameter being indicative of a spread of a spatial distribution of the plurality of lesions of the anatomical structure; calculating the prediction of the medical target variable based on the spread parameter; and providing the prediction of the medical target variable.

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: 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 method for operating a medical imaging apparatus includes acquiring a first image data set of a region of interest of the patient; automatically evaluating the first image data set using at least one evaluation algorithm yielding evaluation information, regarding at least one type of findings, describing a presence of at least one finding of the type requiring the acquisition of a second image data set and at least one imaging parameter for the second image data set; automatically notifying a user, upon the evaluation information indicating the presence of a required second image data set, of the required acquisition of the second image data set; and acquiring the second image data set acquiring the second image data set after confirmation of acquisition of the second image data set by the user, in a same examination process, using the at least one imaging parameter of the evaluation information.

Abstract: The method comprises receiving an image sequence of the subject from the camera during the medical imaging scan; receiving at least one of the current position or velocity of the patient table during the medical imaging scan; performing a motion tracking analysis of the image sequence to extract a motion model, wherein at least one of the motion tracking analysis or the motion model is tailored to the body region of interest and takes into account the at least one of the current patient table position or velocity; and analysing the motion model to detect subject motion and, if the detected motion is above a threshold, at least one of adapting the medical imaging examination or issuing an alert.

Abstract: A method is for image data processing. In an embodiment, the method includes providing a 3D medical image data record, which relates to an elongated anatomical structure, a center line of the elongated anatomical structure being defined in the 3D medical image data record; defining at least one curved slice in the 3D medical image data record, the at least one curved slice winding around the center line; scanning at least one part of the 3D medical image data record into the at least one curved slice; and unrolling the at least one curved slice, into which the at least one part of the 3D medical image data record was scanned, at least one unrolled flat slice being determined. An image data processing unit is also for image data processing and a medical imaging apparatus includes the image data processing unit.

Abstract: A method is for obtaining at least one feature of interest, especially a biomarker, from an input image acquired by a medical imaging device. The at least one feature of interest is the output of a respective node of a machine learning network, in particular a deep learning network. The machine learning network processes at least part of the input image as input data. The used machine learning network is trained by machine learning using at least one constraint for the output of at least one inner node of the machine learning network during the machine learning.

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.