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: One or more example embodiments of the present invention relates to a mammography system for recording an X-ray recording dataset of a region of interest of a breast of an examinee, comprising a compression unit for fixing the breast for the recording; and an interrupt unit triggerable by the examinee and connected to the compression unit and an X-ray source, the interrupt unit being configured to release the fixation and to stop the X-ray radiation.

Abstract: A method for generating result slice images with at least partially different slice thickness based on a tomosynthesis image data set of a breast includes generating average value slices and maximum value slices (MIP) based on the tomosynthesis image data set, frequency dividing the average value slices into low-pass filtered and high-pass filtered average value slices, high-pass filtering of maximum value slices to form high-pass filtered maximum value slices, mixing high-pass filtered maximum value slices and high-pass filtered average value slices to form mixed high-pass filtered maximum value slices, combining the low-pass filtered average value slices with the mixed high-pass filtered maximum value slices to form the result slice images, and applying a moving maximum value across a selected thickness of maximum value slices or across a selected thickness of mixed high-pass filtered maximum value slices.

Abstract: Systems and methods for generating a synthetic image are provided. An input medical image in a first modality is received. A synthetic image in a second modality is generated from the input medical image. The synthetic image is upsampled to increase a resolution of the synthetic image. An output image is generated to simulate image processing of the upsampled synthetic image. The output image is output.

Abstract: One or more example embodiments of the present invention relates to a breast fixation element for a mammography system having a flat elastic surface element including a fastening element for fixing the breast to a support surface of the mammography system, wherein a shape of the breast fixation element is adapted to a shape of the breast.

Abstract: A method is disclosed for generating an image. An embodiment of the method includes detecting a first projection data set via a first group of detector units, the first group including a first plurality of first detector units, each having more than a given number of detector elements; detecting a second projection data set via a second group of detector units, the second group including a second plurality of second detector units, each including, at most, the given number of detector elements; reconstructing first image data based on the first projection data set; reconstructing second image data based on the second projection data set; and combining the first image data and the second image data. A non-transitory computer readable medium, a data processing unit, and an imaging device including the data processing unit 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 comprises: applying a first trained function to first input data to generate first output data, the first output data including first key elements; receiving second input data, the second input data being an X-ray image of an examination region acquired using a first collimation region; applying a second trained function to the second input data to generate second output data, the second output data including second key elements; receiving third input data in response to an incomplete set of second key elements, the third input data including the second key elements and an X-ray image of the examination region acquired using the first collimation region; applying a third trained function to the third input data to generate third output data, the third output data including an estimated third key element to complete the set of second key elements; and providing a complete set of second key elements.

Abstract: One or more example embodiments relates to a computer-implemented method for providing key elements of the examination region in an X-ray image.

Abstract: A method for controlling an FFS X-ray system comprises: simulating a beam geometry of the X-ray beam at a specified FFS deflection onto the detector during recording of a projection image; determining whether cross-radiation is present in a region around the detector by the simulated beam geometry of the X-ray beam; generating FFS control data for the recording of the projection image, wherein the FFS control data either (i) causes FFS deflection that is reduced relative to the specified FFS deflection for the recording of the projection image in the event of the cross-radiation being present for the recording of the projection image or (ii) causes the specified FFS deflection otherwise; repeating the simulating, the determining and the generating FFS control data for at least one further recording of a projection image; and generating a control data set including the FFS control data, for controlling an FFS X-ray system.

Abstract: A method is for creating a synthetic mammogram based upon a dual energy tomosynthesis recording of an examination region. In an embodiment the method includes making a low energy tomosynthesis recording with a first X-ray energy spectrum; making a high energy tomosynthesis recording with a second X-ray energy spectrum of relatively higher energy compared with the first X-ray energy spectrum, wherein the examination region includes a contrast medium distribution; determining a subtraction volume based upon the high energy tomosynthesis recording and the low energy tomosynthesis recording; generating a three-dimensional probability map with a weighting factor per voxel based upon the subtraction volume; and creating a synthetic mammogram based upon the three-dimensional probability map.

Abstract: One or more example embodiments relates to a method for calibrating an X-ray emitter having a cathode, an anode and a coil, wherein the coil is connected to a conductor arrangement through which an electrical function current is guided through the coil. The method comprises measuring an induction current that is induced in the coil at the conductor arrangement of the coil; calculating a compensation current for an effecting coil of the X-ray emitter based on the measured induction current, the effecting coil configured to change an electron beam between the cathode and the anode, wherein the compensation current is calculated such that a magnetic field that induces the induction current during the measuring is compensated using a magnetic field that is produced by the compensation current in the effecting coil; and applying the compensation current in the effecting coil.

Abstract: Systems and methods for generating a synthetic image are provided. An input medical image in a first modality is received. A synthetic image in a second modality is generated from the input medical image. The synthetic image is upsampled to increase a resolution of the synthetic image. An output image is generated to simulate image processing of the upsampled synthetic image. The output image is output.

Abstract: A method and a system are for image noise reduction. In an embodiment, the method includes producing a recorded image; establishing an amount of noise of the recorded image; decomposing the amount of noise into a number of N frequency-dependent noise components for N frequency bands, the number of N frequency-dependent noise components including respective data points respectively reproducing noise, of the amount of noise in the recorded image, for the respective frequency bands of the N frequency bands; examining the number of N frequency-dependent noise components for outlier data points, where an intensity lies outside a range of values, and forming moderated noise components by moderation of values of the outlier data points established in the examining of the number of N frequency-dependent noise components; and subtracting the moderated noise components from the recorded image.

Abstract: A method for generating result slice images with at least partially different slice thickness based on a tomosynthesis image data set of a breast includes generating average value slices and maximum value slices (MIP) based on the tomosynthesis image data set, frequency dividing the average value slices into low-pass filtered and high-pass filtered average value slices, high-pass filtering of maximum value slices to form high-pass filtered maximum value slices, mixing high-pass filtered maximum value slices and high-pass filtered average value slices to form mixed high-pass filtered maximum value slices, combining the low-pass filtered average value slices with the mixed high-pass filtered maximum value slices to form the result slice images, and applying a moving maximum value across a selected thickness of maximum value slices or across a selected thickness of mixed high-pass filtered maximum value slices.

Abstract: A method and a system are for image noise reduction. In an embodiment, the method includes producing a recorded image; establishing an amount of noise of the recorded image; decomposing the amount of noise into a number of N frequency-dependent noise components for N frequency bands, the number of N frequency-dependent noise components including respective data points respectively reproducing noise, of the amount of noise in the recorded image, for the respective frequency bands of the N frequency bands; examining the number of N frequency-dependent noise components for outlier data points, where an intensity lies outside a range of values, and forming moderated noise components by moderation of values of the outlier data points established in the examining of the number of N frequency-dependent noise components; and subtracting the moderated noise components from the recorded image.

Abstract: A method is for reconstructing an image from recording data. In an embodiment, the method includes providing or measuring recording parameters with which the recording data was created; generating a preprocessing filter for the recording data based on the recording parameters; prefiltering the recording data with the preprocessing filter, to create prefiltered recording data; and reconstructing the image from the prefiltered recording data. A prefilter-generating unit, an apparatus corresponding to the method, a control facility and an image-taking system are also disclosed.

Abstract: A method is disclosed for generating an image. An embodiment of the method includes detecting a first projection data set via a first group of detector units, the first group including a first plurality of first detector units, each having more than a given number of detector elements; detecting a second projection data set via a second group of detector units, the second group including a second plurality of second detector units, each including, at most, the given number of detector elements; reconstructing first image data based on the first projection data set; reconstructing second image data based on the second projection data set; and combining the first image data and the second image data. A non-transitory computer readable medium, a data processing unit, and an imaging device including the data processing unit are also disclosed.

Abstract: A method is for generating a first synthetic mammogram. In an embodiment, the method includes acquiring a tomosynthesis dataset including a plurality of projection images of a tissue region from different projection directions in a projection angle range; reconstructing a slice image dataset based on the tomosynthesis dataset; localizing tissue changes in the slice image dataset; determining a first projection direction for a first synthetic mammogram based on the spatial distribution of the tissue changes in the slice image dataset and generating the first synthetic mammogram in the first projection direction based on the tomosynthesis dataset.

Abstract: An x-ray imaging system includes an x-ray source configured to emit x-ray radiation towards a sample, and a primary detector configured to detect x-ray radiation from the x-ray source passing through the sample. The x-ray imaging system also includes a secondary detector configured to detect x-ray radiation from the x-ray source scattered in the sample, and imaging optics configured to guide x-ray radiation scattered in the sample onto the secondary detector.