Abstract: A computer-implemented method, comprising: receiving input data including a medical image and an in-image annotation; applying a first function to the input data to determine a relevance value of pixels in the image and a relevance map; applying a second function to the medical image to generate a de-identified medical image; applying a trained function to the medical image and the de-identified medical image to determine a first property in the medical image and a second property in the de-identified medical image; applying a comparison function to the first property and the second property to determine a similarity value, wherein in response to the similarity value being below a similarity threshold, the relevance map is adjusted and the applying of the second function, the applying of the trained function and the applying of the comparison function are repeated; and providing the de-identified medical image and the in-image annotation.

Abstract: A computer-implemented method comprises: receiving input data, wherein the input data includes the X-ray image dataset, which includes an X-ray image and first metadata; applying a trained function to the input data to generate output data, wherein the output data includes second metadata, and wherein the first metadata and the second metadata are compared; and providing the output data, wherein the first metadata are confirmed in case the first metadata and the second metadata agree, or the first metadata are suggested to be corrected with the second metadata in case the first metadata and the second metadata do not agree.

Abstract: One or more example embodiments of the present invention relates to a method for determining a Background Parenchymal Enhancement (BPE) in a contrast medium-enhanced X-ray examination of a breast comprising providing images of the X-ray examination, the images having been taken after administration of the contrast medium, the images comprising at least one low energy (LE) image taken at a predetermined low X-ray energy and a high energy (HE) image taken at a predetermined high X-ray energy, creating an iodine image from the LE image and the HE image and calculating a volumetric BPE as a sum of all pixel values in the iodine image for which the following two conditions apply i) iodine enrichment is present in the iodine image, and ii) fibroglandular tissue (FGT) is present. The method further includes obtaining BPE result data based on the iodine image and volumetric BPE and outputting the BPE result data.

Abstract: A computer program, a system and a method for normalizing medical images from a type of image acquisition device using a machine learning unit are disclosed. An embodiment of the method includes receiving a set of image data with images; decomposing each of the images of the set of images into components by incorporating at least information from different settings of the image acquisition device-specific image processing algorithms; and normalizing each of the components via a machine learning unit by processing at least information from the different settings of the image acquisition device-specific processing algorithms to provide a set of normalized images with a relatively decreased variability score.

Abstract: According to a method for correcting a 2D measurement value is described, 2D image data of an examination object is received. Landmarks in the 2D image data are detected, and 2D positions of the landmarks are calculated. A corrected measurement value of the examination object is predicted, using a trained model, which depends on the received 2D image data, the estimated 2D positions of the landmarks and a reference parameter of a reference 3D orientation of the examination object.

Abstract: A method for automatically determining spine deformation from an image showing a number of vertebrae of the spine, comprises: detecting center points of the number of vertebrae shown in the image; constructing a center line based on the center points; computing a local tilt at points along the center line; determining positive and negative tilt-maxima of the local tilt and selecting two reference vertebrae having center points closest to the positive and negative tilt-maxima; segmenting an upper endplate of the cranial reference vertebra; segmenting a lower endplate of the caudal reference vertebra; computing an angle between the upper endplate and the lower endplate; and outputting the angle.

Abstract: In a method, comparison features are extracted from labeled reference image data. Features are also extracted from the image data. A statistical comparison of the comparison features with the features then takes place. On the basis of the statistical comparison and a quality criterion, the quality of the AI-based result data is determined. A method for correcting result data is additionally described. Furthermore, a method for AI-based acquisition of result data on the basis of measured examination data is described. Also described is a validation entity. An entity for correcting result data is additionally described. Furthermore, an entity for acquiring result data is described. Also described is a medical imaging entity.

Abstract: With a device and an associated method, an ultrasound unit is coupled with a coupling unit to an existing x-ray unit. The ultrasound unit is guided with the aid of controllable elements of a positioning unit of the x-ray unit on a patient for an ultrasound examination. In this manner X-ray recordings and ultrasound recordings of an object can be produced.

Abstract: A method is for determining a positioning quality of an external apparatus inserted into a patient's body. In the method, image data of the patient's body is detected; an at least one first anatomical landmark is detected in or with a first subregion, in which the external apparatus is positioned as expected; at least a first subsection of the external apparatus is sought in or with the at least one first subregion; a second anatomical landmark is detected in or with at least one second subregion; at least one second subsection is then detected based upon the already localized subsection; and a quality of the positioning of the at least one second subsection is determined by measuring a suitable dimension between the localized at least one second subsection and the at least one second landmark. A training method, a positioning quality determination facility and a training facility are also disclosed.

Abstract: A computer program, a system and a method for normalizing medical images from a type of image acquisition device using a machine learning unit are disclosed. An embodiment of the method includes receiving a set of image data with images; decomposing each of the images of the set of images into components by incorporating at least information from different settings of the image acquisition device-specific image processing algorithms; and normalizing each of the components via a machine learning unit by processing at least information from the different settings of the image acquisition device-specific processing algorithms to provide a set of normalized images with a relatively decreased variability score.

Abstract: A method is for determining result values based upon a skeletal medical imaging recording. In an embodiment, the method includes providing a skeletal medical imaging recording; automatically determining reference points in the image recording; and calculating an orthopedic result value based at least upon a spacing between two reference points and/or upon an angle defined by reference points. A corresponding device, a corresponding diagnostic station for the assessment of a skeletal medical imaging recording and a corresponding medical imaging system also are disclosed in embodiments.

Abstract: A method is for operating an X-ray device. In an embodiment, the method includes acquiring a sequence of images of a patient; moving an acquisition arrangement including at least one X-ray tube assembly, during the acquiring of the sequence of images, along the patient in a scanning direction; evaluating at least two different images, showing at least one common feature of the patient, to determine depth information for the at least one common feature; and actuating a collimator aperture of a collimator of the X-ray tube assembly, as a function of position information describing a position of the acquisition arrangement in the scanning direction, to change an aperture angle of a radiation field generated by the X-ray tube assembly in the scanning direction.

Abstract: A method is for operating an X-ray device. In an embodiment, the method includes acquiring a sequence of images of a patient; moving an acquisition arrangement including at least one X-ray tube assembly, during the acquiring of the sequence of images, along the patient in a scanning direction; evaluating at least two different images, showing at least one common feature of the patient, to determine depth information for the at least one common feature; and actuating a collimator aperture of a collimator of the X-ray tube assembly, as a function of position information describing a position of the acquisition arrangement in the scanning direction, to change an aperture angle of a radiation field generated by the X-ray tube assembly in the scanning direction.

Abstract: A method ascertains a time for a fresh calibration for ascertaining up-to-date calibration parameters of an x-ray device. The x-ray device has multiple degrees of freedom of movement for its recording arrangement. X-ray images of a calibration phantom are recorded for recording positions of the recording arrangement and are evaluated to ascertain the calibration parameters allowing ascertainment of geometry parameters. In multiple operating phases, situated between two calibrations, of a further x-ray device of identical design to the x-ray device, a piece of use information describing the accumulated use of the degrees of freedom of movement during the operating phase and a piece of difference information describing the difference in the calibration parameters between the calibrations delimiting the operating phase, is ascertained and used or determining the time for a fresh calibration.

Abstract: A method is for determining result values based upon a skeletal medical imaging recording. In an embodiment, the method includes providing a skeletal medical imaging recording; automatically determining reference points in the image recording; and calculating an orthopedic result value based at least upon a spacing between two reference points and/or upon an angle defined by reference points. A corresponding device, a corresponding diagnostic station for the assessment of a skeletal medical imaging recording and a corresponding medical imaging system also are disclosed in embodiments.

Abstract: A method for the automatic generation of synthetic projections of an examination object from at least one three-dimensional data set acquired by way of a medical imaging system. The three-dimensional data set is used as a basis for determining position information relating to the arrangement of structures of the examination object and at least one synthetic projection based on the position information. We also describe a projection-image-ascertaining facility for the automatic generation of synthetic projections and a computer program product for executing the method.

Abstract: The embodiments relate to a method, an apparatus, and a computer program for evaluating an x-ray image of a breast produced during a mammography. In order to simplify the evaluation of such an x-ray image in respect of the breast density, a method is proposed to automatically determine the masking risk caused by the mammographically dense tissue and to use this for categorizing, describing, and/or representing the breast density.

Abstract: A method is provided for the automatic determination of at least one joint load information item concerning a joint of a patient, wherein at least one image data set of the loaded joint is recorded by an imaging apparatus. Several image data sets of the joint, of which at least one is three-dimensional, are recorded in each case for different loading states by the imaging apparatus. By combined evaluation of the image data sets, the at least one joint load information item is determined.

Abstract: A recording and reconstruction of image data of a predetermined object of an examination object is performed by an x-ray system. The x-ray system determines a number of elliptical cylinders so that the object is arranged entirely in the volumes of the cylinders. Data within each of the cylinders is recorded by the x-ray system. The image data of the object is reconstructed based on the data recorded for each of the cylinders.

Abstract: The embodiments relate to a method, an apparatus, and a computer program for evaluating an x-ray image of a breast produced during a mammography. In order to simplify the evaluation of such an x-ray image in respect of the breast density, a method is proposed to automatically determine the masking risk caused by the mammographically dense tissue and to use this for categorizing, describing, and/or representing the breast density.