Abstract: A method is for processing parameters of a machine-learning method for providing a correction dataset for motion correction of a CT image dataset of an object that moves during recording thereof. A training dataset with a number of reference image datasets for introduction into the machine-learning method is provided. Iteration is performed. In each of the iteration steps, a correction dataset is determined for each of the reference image datasets via the machine-learning method and a result of a cost function dependent on the correction datasets determined in this iteration step is ascertained. The iteration is terminated or a parameter of the machine-learning method is altered in dependence on the result of the cost function. A reconstruction method further uses the processed machine-learning method.

Abstract: A method and energy-sensitive CT device are disclosed for motion correction of a computed tomography image. In an embodiment, the method includes: provisioning spectral CT data of an examination region with a moving object, the spectral CT data being recorded with an energy-sensitive CT device and including CT data at at least one energy level, and the at least one energy level of the CT data being adapted to a structure in the moving object; identifying the structure in the CT data at at least one energy level, of the at least one energy level, adapted to the structure; calculating a motion vector field of the structure identified; and motion correcting the spectral CT data by the motion vector field calculated to produce a motion-corrected CT 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.

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 processing parameters of a machine-learning method for providing a correction dataset for motion correction of a CT image dataset of an object that moves during recording thereof. A training dataset with a number of reference image datasets for introduction into the machine-learning method is provided. Iteration is performed. In each of the iteration steps, a correction dataset is determined for each of the reference image datasets via the machine-learning method and a result of a cost function dependent on the correction datasets determined in this iteration step is ascertained. The iteration is terminated or a parameter of the machine-learning method is altered in dependence on the result of the cost function. A reconstruction method further uses the processed machine-learning method.

Abstract: A method and energy-sensitive CT device are disclosed for motion correction of a computed tomography image. In an embodiment, the method includes: provisioning spectral CT data of an examination region with a moving object, the spectral CT data being recorded with an energy-sensitive CT device and including CT data at at least one energy level, and the at least one energy level of the CT data being adapted to a structure in the moving object; identifying the structure in the CT data at at least one energy level, of the at least one energy level, adapted to the structure; calculating a motion vector field of the structure identified; and motion correcting the spectral CT data by the motion vector field calculated to produce a motion-corrected CT image.

Abstract: The invention relates to a patient's device having an at least unidirectional, wireless interface for receiving a data signal. The wireless interface is adapted to receive medical or operational data from a medical device, in particular an implantable medical device like a cardiac pacemaker or a cardioverter/defibrillator, a data communication interface for accessing a wide area network or a public telecommunication network or both. The device comprises an automatic routing/dialling module connected to the data communication interface, adapted to establish an automatic access to a modem connected to the data communication interface by automatically selecting one of a plurality of possible connection parameters. The connection parameters are selected from at least one of an individual modem, if more than one modem is connected to the data communication interface, and a prefix number for a remote access to a remote device over a public network automatically selecting a dial-up telephone number.

Abstract: The invention relates to a patient's device having an at least unidirectional, wireless interface for receiving a data signal. The wireless interface is adapted to receive medical or operational data from a medical device, in particular an implantable medical device like a cardiac pacemaker or a cardioverter/defibrillator, a data communication interface for accessing a wide area network or a public telecommunication network or both. The device comprises an automatic routing/dialling module connected to the data communication interface, adapted to establish an automatic access to a modem connected to the data communication interface by automatically selecting one of a plurality of possible connection parameters. The connection parameters are selected from at least one of an individual modem, if more than one modem is connected to the data communication interface, and a prefix number for a remote access to a remote device over a public network automatically selecting a dial-up telephone number.

Abstract: A method of calculating the heart rate variability of the human heart to be used in an ECG monitor comprises the following procedural steps: scanning of the ECG signal received from the ECG monitor during a scanning interval, determining from the scanned ECG signals a number of discrete measuring values representative of the heart rate variability, and evaluating these measuring values on the basis of the Fourier transformation, wherein the frequency spectre of the measuring values is calculated from the Fourier coefficients of the Fourier transformation, which are for their turn calculated from a combination of said measuring values with the sinus- and cosinus-shaped Fourier vectors of the Fourier transformation wherein the Fourier vectors are involved in the calculation in the form of a number of discrete real numerical vector values, and replacing of the numerical vector values for calculating the frequency spectrumof the scanned measuring values by rough, integral approximate vector values of a limite

Abstract: A method of calculating the heart rate variability of the human heart to be used in an ECG monitor comprises the following procedural steps: