Abstract: A method for correcting material-induced effects in CT images, comprising: providing a CT scan dataset including CT scan data of an object at at least two different energies; calculating a plurality of first material density values for a first substance type from the CT scan data of the CT scan dataset; calculating a plurality of correction factors based on the plurality of first material density values, wherein each correction factor is assigned to a second material density value for a second substance type; and calculating CT images from the plurality of first and second material density values, wherein at least one of the CT images is corrected via the plurality of correction factors.

Abstract: A method for generating training data for training a deep learning algorithm, comprising: receiving medical imaging data of an examination area including a first part and a second part of a symmetric organ; splitting the medical imaging data along a symmetry plane or a symmetry axis into a first dataset and a second dataset, wherein the first dataset includes the medical imaging data of the first part and the second dataset includes the medical imaging data of the second part; mirroring the second dataset along the symmetry plane or the symmetry axis; generating the training data by stacking the first dataset and the mirrored second dataset; and providing the training data.

Abstract: In an embodiment of a motion correction method, a first virtual non-contrast X-ray image of a region under examination is determined based upon first spectral raw X-ray data associated with a first contrast distribution, via material decomposition. In addition, a second virtual non-contrast X-ray image of the region under examination is determined based upon second spectral raw X-ray data associated with a second contrast distribution, differing from the first contrast distribution, via material decomposition. Then the first virtual non-contrast X-ray image is registered with the second virtual non-contrast X-ray image to determine a transformation field between the two virtual non-contrast X-ray images. Finally, based upon the determined transformation field, first X-ray image data based on the first raw X-ray data is registered with second X-ray image data based on the second raw X-ray data. An X-ray imaging method, a motion correction device and an X-ray imaging system are also discussed.

Abstract: A computer-implemented segmentation method for segmenting a core and a penumbra in a four-dimensional computed tomography perfusion dataset of ischemic tissue in an image region of a patient, includes determining at least one parameter map for at least one perfusion parameter from the computed tomography perfusion dataset; and using the at least one parameter map and the computed tomograph perfusion dataset as input data to a trained function to determine output data, the output data including segmentation information of the penumbra and the core.

Abstract: A CT perfusion data determination method is described. In the CT perfusion data determination method, raw X-ray data affected by contrast agent and generated by way of a spectral or multi-energy CT image acquisition method is recorded from an examination region, a plurality of images having been acquired from the examination region at successive time instants. Based on the recorded raw X-ray data, a virtual baseline image is determined by calculating virtual native image data through the application of a material decomposition. Finally a temporal course of a contrast agent concentration in the examination region is determined based upon the raw X-ray data affected by contrast agent and the virtual baseline image. A CT perfusion measurement method is also described. Furthermore, a CT perfusion data determination facility is described. Moreover, a computed tomography system is described.

Abstract: A method and system are for analyzing a plurality of interconnected blood vessels, preferably for providing output data for the analysis of a plurality of interconnected blood vessels, e.g. for vessel segmentation, vessel labeling, and vessel occlusion detection. The output data include a graphical representation of a vessel tree model of a patient. The method of an embodiment, in the most general terms includes receiving input data, e.g. via an interface; generating output data by applying algorithmic operations to the input data; and providing the output data, e.g. via an interface.

Abstract: In an embodiment of a motion correction method, a first virtual non-contrast X-ray image of a region under examination is determined based upon first spectral raw X-ray data associated with a first contrast distribution, via material decomposition. In addition, a second virtual non-contrast X-ray image of the region under examination is determined based upon second spectral raw X-ray data associated with a second contrast distribution, differing from the first contrast distribution, via material decomposition. Then the first virtual non-contrast X-ray image is registered with the second virtual non-contrast X-ray image to determine a transformation field between the two virtual non-contrast X-ray images. Finally, based upon the determined transformation field, first X-ray image data based on the first raw X-ray data is registered with second X-ray image data based on the second raw X-ray data. An X-ray imaging method, a motion correction device and an X-ray imaging system are also discussed.

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: 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.