Abstract: A computer-implemented method comprises: receiving a measurement data set, the measurement data set including energy resolved data based on a computed tomography scan of the patient; reconstructing a morphology preserving image data set based on a first photon-energy band or a first combination of photon-energy bands described by the measurement data set; segmenting a blood pool within a myocardium in the morphology preserving image data set; reconstructing a contrast agent map based on a second photon-energy band or a second combination of photon-energy bands described by the measurement data set; determining a reference value based on at least one pixel or voxel of the contrast agent map within the segmented blood pool; and determining a respective myocardial extracellular volume fraction depending on the reference value and a value given for at least one respective pixel or voxel outside the segmented blood pool by the contrast agent map.

Abstract: A method for reconstructing CT images, comprises: providing CT recording data; reconstructing overlapping partial images; establishing displacement vectors for registering overlap regions of the partial images; interpolating a displacement vector field for each partial image from associated sets of the displacement vectors of the two side regions; creating an output image dataset based on the CT recording data and the displacement vector fields; and outputting the output image dataset.

Abstract: One or more example embodiments provides a method for evaluating an angiographic dual-energy computed tomography dataset recorded using a contrast agent comprising iodine to determine a quantitative calcium score.

Abstract: A computer-implemented method for evaluating an image data set of an imaged region comprises: determining, from the image data set, at least two processed data sets having different image data content; applying a first sub-algorithm, of an evaluation algorithm, to a first of at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets; applying a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets; determining quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, wherein the third sub-algorithm uses both the first intermediate result and the second intermediate result as input data.

Abstract: A method is for generating image data of an examination object via a computed tomography system including a data processing unit; an X-ray radiation source and an X-ray radiation detector suspended on a support and mounted to be rotatable about a z-axis; and an examination table for supporting the examination object and a reference object arranged in a fixed position relative to the examination table. The method includes generating a raw data set by displacing the X-ray radiation source and the X-ray radiation detector relative to the examination object. During generation of the raw data set, at least one part of the examination object is sampled together with at least one part of the reference object. The sampling of the at least one part of the reference object is used to compensate at least in part for the influence of movement errors during the displacement.

Abstract: The present invention relates to a new chemical synthesis, intermediates and catalysts useful for the preparation of the neprilysin (NEP) inhibitor sacubitril. It further relates to new intermediate compounds and their use for said new chemical synthesis route.

Abstract: An imaging method is described for generating image data of an examination region of an object that is to be examined. First X-ray projection measurement data of the examination region is acquired using a first X-ray energy spectrum and at least second X-ray projection measurement data of the examination region is acquired using a second X-ray energy spectrum which is different from the first X-ray energy spectrum. A priori image data is reconstructed based on at least the first X-ray projection measurement data and a location-dependent distribution of X-ray attenuation values. A basis material decomposition is performed based on the first X-ray projection measurement data and the at least second X-ray projection measurement data. A location-dependent weighting of the basis materials is determined as a function of the location-dependent distribution of the X-ray attenuation values. An image for the examination region is determined by reconstructing virtual basis-material-weighted image data.

Abstract: A method is for actuating a medical imaging device including a radiation source, embodied during a rotational movement around an axis of rotation to irradiate an irradiation region from a plurality of angular positions. In an embodiment, the method includes capturing an object position of a moving object relative to the irradiation region. The Method further includes actuating the medical imaging device based upon the captured object position of the moving object, such that the intensity of the radiation emitted by the radiation source for a first partial number of the plurality of angular positions in a first angular range round the axis of rotation is reduced relative to a second partial number of the plurality of angular positions in a second angular range around the axis of rotation, he captured object position being included in the first angular range.

Abstract: A method for controlling a medical apparatus. An embodiment of the method includes providing, via an interface, a first function dataset of a patient, measured within a first time interval; applying, via a processor, a trained function to the measured first function dataset provided, to estimate a second function dataset of the patient, predicted for a second time interval, wherein at least one parameter of the trained function is adapted based upon a comparison between a predicted, second training function-dataset for a second training time-interval, the second training function-dataset being predicted based upon a first training function-dataset of a training patient for a first training time-interval, and a comparison function-dataset of the training patient for the second training time-interval, and wherein the first training function-dataset and the comparison function-dataset are associated; and controlling, via a controller, the medical apparatus based upon the estimate.

Abstract: A method is for generating image data of an examination object via a computed tomography system including a data processing unit; an X-ray radiation source and an X-ray radiation detector suspended on a support and mounted to be rotatable about a z-axis; and an examination table for supporting the examination object and a reference object arranged in a fixed position relative to the examination table. The method includes generating a raw data set by displacing the X-ray radiation source and the X-ray radiation detector relative to the examination object. During generation of the raw data set, at least one part of the examination object is sampled together with at least one part of the reference object. The sampling of the at least one part of the reference object is used to compensate at least in part for the influence of movement errors during the displacement.

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 method is for checking a medical image with regard to a processing of the medical image via an image processing module. The method includes providing at least one input requirement of the image processing module relating to at least one image parameter of the medical image; providing the at least one image parameter of the medical image; and checking whether the at least one image parameter fulfills the at least one input requirement.

Abstract: A method is for the transmission of patient-specific data to an examination log adjustment unit. In an embodiment, the method includes providing a user interface via a software application; receiving patient-specific data of a patient via the user interface, the patient-specific data being based on an input into the user interface; storing the patient-specific data received; and transmitting the patient-specific data to the examination log adjustment unit, the examination log adjustment unit being embodied to adjust an examination log for a medical imaging examination of the patient based upon the patient-specific data.

Abstract: A computer tomograph, a method, and a computer program product are disclosed. A computer tomograph is disclosed for scanning an image of at least one examination region of a patient, including a scanning unit which can be rotated about a longitudinal axis. The scanning unit includes an X-ray detector and an X-ray emitter. The extension of an X-ray beam emitted by the X-ray emitter is controlled for scanning purposes along the longitudinal axis using at least one patient-dependent control signal. By controlling the extension of the X-ray beam, the dose applied to the patient is also controlled such that the dose can be used as efficiently as possible. The control further allows the pitch to be matched to the patient-dependent control signal while scanning in the spiral mode.

Abstract: A method is described for the determination of a calcium score for a patient to be examined with the aid of a CT system. The method is used to define patient-specific CT-acquisition parameters. In addition, material parameters for a model method for the generation of synthetic image data for virtual CT-acquisition parameters are calibrated using phantom image data recorded with reference CT-acquisition parameters. A calcium score assigned to synthetic phantom image data corresponds to a calcium score determined with phantom image data recorded with reference CT-acquisition parameters. Next, CT-projection-measurement data is acquired for a region of interest using the patient-specific CT-acquisition parameters. The acquired CT-projection-measurement data is used to generate synthetic image data using the calibrated model method. Finally, a calcium score is determined using a standard method on the basis of the synthetic image data. Also described is a calcium-score-determining device.

Abstract: The present invention relates to a new chemical synthesis, intermediates and catalysts useful for the preparation of the neprilysin (NEP) inhibitor sacubitril. It further relates to new intermediate compounds and their use for said new chemical synthesis route.

Abstract: The present invention relates to a new chemical synthesis, intermediates and catalysts useful for the preparation of the neprilysin (NEP) inhibitor sacubitril. It further relates to new intermediate compounds and their use for said new chemical synthesis route.

Abstract: A method is for generating CT image data with spectral information from an examination region of a patient. According to an embodiment, the examination region is exposed to polychromatic X-radiation. Data from the examination region, including a first projection measurement data record and at least one second projection measurement data record, is captured via a photon counting detector. At least one second projection measurement data record with reduced resolution is generated based upon the at least one second projection measurement data record. The first and at least one second projection measurement data records are then transmitted to an image generation unit. Finally, the resolution of the first projection measurement data record is transferred onto the at least one second projection measurement data record with reduced resolution and/or an image data record based on the at least one second projection measurement data record with reduced resolution. A system is also disclosed.

Abstract: A multi-spectral CT imaging method, preferably a CT imaging method, is described. Spectrally resolved projection scan data is acquired from a region to be imaged of an examination object. The data is assigned to a plurality of pre-determined different partial spectra. Spectrally resolved image data is reconstructed with a plurality of attenuation values for each image point of the region to be imaged. The attenuation values are each assigned to one of the pre-determined different partial spectra. Furthermore, an extremal attenuation value is determined for each image point on the basis of the plurality of attenuation values. A representative image data set is determined such that the determined extremal attenuation value is assigned to each image point.

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.