Abstract: A method for determining a local tissue function of tissue in a body region of interest of an examination object is disclosed. In an embodiment, the method includes segmentation of an outer contour of the tissue using at least one medical image recording representing the body region of interest of the examination object comprising the tissue; subdivision of the segmented tissue into at least two tissue regions; and ascertaining a function parameter relating to the tissue function for each of the at least two tissue regions. Embodiments also relate to a corresponding computing unit for determining a tissue function of tissue, a corresponding medical imaging system, a computer program and a computer-readable data carrier.

Abstract: A method is for producing a CT image data set via a detection unit with a photon-counting X-ray detector and configured to convert detected X-rays into measurement signals resolved at least into a first and a second energy range and, via an X-ray source unit, configured to emit X-rays having a first energy spectrum and having a different second energy spectrum. The method includes adapting the first and the second energy ranges, at least one limiting energy of each energy range being adapted; emitting X-rays of the first and second energy spectrum; detecting the emitted X-rays, at least one measurement signal being generated as a function of each respective energy range and the respective energy spectrum; producing the spectral CT image data set based upon the generated first and second measurement signals with the assistance of a spectral image processing technique; and outputting the spectral CT image data set.

Abstract: Systems and methods are provided for indicating recommended settings for data acquisition in a medical scanner. A scan protocol including a first parameter is identified. A first subset of values is determined for the first parameter as a function of the scan protocol. A first plurality of values is displayed for the first parameter with the first subset of values highlighted. A first selection is received of a first selected value for the first parameter. Image data is generated using the first selection.

Abstract: The present disclosure relates to storing data in a computer system. The computer system comprising a main memory coupled to a processor and a cache hierarchy. The main memory comprises a predefined bit pattern replacing existing data of the main memory. Aspects include storing the predefined bit pattern into a reference storage of the computer system. At least one bit in a cache directory entry of a first cache line of the cache hierarchy can be set. Upon receiving a request to read the content of the first cache line, the request can be redirected to the predefined bit pattern in the reference storage based on the value of the set bit of the first cache line.

Abstract: Embodiments for global bit line latch performance and power optimization are described herein. An aspect includes a bit line including a first section, a second section, and coupling circuitry arranged between the first section and the second section, the coupling circuitry adapted to causes a voltage drop between the first section and the second section, and at least one logic element including a first branch connected to a first power supply and a second branch connected to a second power supply, wherein the first branch is connected to the first section of the bit line, and wherein the second branch is connected to the second section of the bit line.

Abstract: A method is for creating a classification unit for the automatic classification of materials. An embodiment of the method includes provision of a learning computing device; provision of a start classification unit; provision of a reference image set including spectral reference recordings with annotated materials; and training of the classification unit with the reference recording set. Furthermore, a classification method is for the automatic classification of materials in an image recording. An embodiment of the classification method includes provision of a trained classification unit; provision of a spectral image recording; examination of the image recording for materials via the classification unit; and identification of the determined materials. Furthermore, a classification unit, a learning computing device, a control device and a medical imaging system are disclosed.

Abstract: One embodiment relates to a method for providing image data of a hollow organ. The method includes applying a first contrast agent to a lumen of the hollow organ, to obtain a contrast agent filling of the lumen; applying a second contrast agent to a blood vessel system, the blood vessel system supplying a wall of the hollow organ, the second contrast agent having a different absorption spectrum than the first contrast agent; generating spectrally resolved computed tomography data of an examination area, the examination area including the hollow organ; calculating first image data indicative of a presence of the first contrast agent and second image data indicative of a presence of the second contrast agent by applying a material separation algorithm onto the spectrally resolved computed tomography data; and providing the image data of the hollow organ including the first image data and the second image data.

Abstract: A method is for training a convolutional neural network of a compensation unit. The method includes: provisioning a machine learning device, the machine learning device being designed for training the convolutional neural network; provisioning a start compensation unit, including an untrained convolutional neural network, on or at the machine learning device; provisioning a training image dataset including a plurality of medical training input images and at least one training output image, wherein a reference object is shown essentially without motion artifacts in the at least one training output image and the reference object concerned is contained in the plurality of medical training input images with different motion artifacts; and training the convolutional neural network of the compensation unit in accordance with a principle of machine learning, using the training image dataset. A compensation unit, a machine learning device, a control device for controlling a medical imaging system are also disclosed.

Abstract: A memory cell arrangement for Random Access Memory (RAM) including one or more RAM cell groups. The RAM cell groups having two or more local bit-lines sharing a Global Bit-Line (GBL), a pre-charging circuit connected to the GBL, a multiplexer connected to multiple GBLs and configured to shift an output of a first GBL from a first bit to a second bit at least in part according to a value of a fuse bit register associated with a second GBL, and at least one pre-charge enabling circuit controlled by a combination of a pre-charge input value applied to all GBLs of the memory cell arrangement and a pre-charge enable signal for the GBL.

Abstract: An X-ray system, in particular a computed tomography system, for acquiring projection data of an examination subject includes one or more X-ray radiation sources, at least one of the one or more X-ray radiation sources including at least one prefilter, the one or more X-ray radiation sources being configured to generate X-ray radiation including at least two X-ray radiation spectra, the at least one prefilter being configured to at least one of spatially distribute or temporally modify the X-ray radiation, and a one or more photon-counting detectors configured to detect the X-ray radiation passing through the examination subject in an energy-resolved manner according to at least two detection thresholds, and generate projection data based on the detection.

Abstract: The present disclosure relates to storing data in a computer system. The computer system comprising a main memory coupled to a processor and a cache hierarchy. The main memory comprises a predefined bit pattern replacing existing data of the main memory. Aspects include storing the predefined bit pattern into a reference storage of the computer system. At least one bit in a cache directory entry of a first cache line of the cache hierarchy can be set. Upon receiving a request to read the content of the first cache line, the request can be redirected to the predefined bit pattern in the reference storage based on the value of the set bit of the first cache line.

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: Embodiments for global bit line latch performance and power optimization are described herein. An aspect includes a bit line including a first section, a second section, and coupling circuitry arranged between the first section and the second section, the coupling circuitry adapted to causes a voltage drop between the first section and the second section, and at least one logic element including a first branch connected to a first power supply and a second branch connected to a second power supply, wherein the first branch is connected to the first section of the bit line, and wherein the second branch is connected to the second section of the bit line.

Abstract: An X-ray imaging method is for generating image data of a field of view of an object to be examined. In the method, firstly an individual imaging protocol is determined for imaging of the object to be examined. Furthermore, first X-ray projection measurement data with a first X-ray energy spectrum and at least one set of second contrast medium-influenced X-ray projection measurement data with a second X-ray energy spectrum, are acquired from the field of view. A third X-ray energy spectrum with a third mean energy is then determined on the basis of the determined individual imaging protocol. Subsequently, preferably pseudo-monoenergetic image data, associated with the third X-ray energy spectrum, is reconstructed on the basis of the acquired first and at least second X-ray projection measurement data as well as the determined imaging protocol. An image data-generating device is also described. A computerized tomography system is described, moreover.

Abstract: A method is for recording a region of interest of an examination object with a computed tomography system including an energy-selective X-ray detector with a number of energy threshold values that can be set by way of an energy threshold values. In an embodiment, the method includes first recording of first projection scan data with a first set of energy thresholds; setting a second set of energy thresholds different from the first set of energy thresholds, based on a temporally variable parameter; and second recording of second projection scan data different from the first projection scan data with the second set of energy thresholds.

Abstract: In one embodiment, a method is for calculating an examination parameter for a computed tomography examination of an area of interest of a patient. The method includes receiving a topogram of the area of interest of the patient; determining fat distribution information by applying a trained machine learning algorithm onto the topogram; and calculating the examination parameter for the computed tomography examination of the area of interest of the patient based on the fat distribution information.

Abstract: A method is for classifying an examination object by way of recordings. In an embodiment, the method includes capturing at least one optical recording of the examination object; determining and quantifying a number of defined characteristics of the examination object based upon an analysis of the optical recordings with the aid of a machine learning method; and affecting the classification of the examination object in respect of a classification criterion, based upon the quantified characteristics with the aid of a machine learning method. Also described are a classification entity and a medical imaging modality.

Abstract: To generate images, in which a first row area of a multiple-row detector is illuminated with a first X-ray spectrum and a second row area of the multiple-row detector, trailing in the direction of travel, is illuminated with a second X-ray spectrum, image data is recorded at a pitch chosen such that one slice image can be reconstructed in each case for a sectional position for the first and the second row area. Correspondingly, for the sectional position, the respective slice image for the first and the second row area is reconstructed. For a third row area, illuminated with the first and the second X-ray spectrum, a reference sectional image is reconstructed as a slice image. To generate motion-reduced first and second spectral images assigned to the first and second row area respectively, the slice images of the first and second row areas are registered to the reference sectional image.

Abstract: A method is for metal artifact reduction in CT image data, the CT image data including multiple 2D projection images acquired using different projection geometries and suitable to reconstruct a 3D image data set of a volume of an imaged object. In an embodiment, the method includes a metal artifact reduction process including at least, acquiring, using a multi-energy CT technique, energy-resolved CT image data associated with multiple energy ranges. At least one result of the multi-energy technique is used in at least one aspect of the metal artifact reduction process.

Abstract: A medical scanner generates a medical image that may have an artifact. A machine-learnt detector, trained from a library of many different types of artifacts in images, detects any artifact in the medical image for a patient. The location of the artifact is highlighted, providing an indication of possible artifact where the image may otherwise appear to represent anatomy or pathology. A machine-learnt network may be applied to the medical image to determine a correction, such as different scan or reconstruction, to remove or reduce the artifact.