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 determining at least one first characteristic value of blood in a patient. In an embodiment, the method includes acquiring, via a computed tomography device, computed tomography data of the patient for at least two energy levels of radiation using multi-energy computed tomography; defining a region of interest including blood, in at least one image data set reconstructed from the computed tomography data acquired; determining, at least in the region of interest defined, attenuation coefficients for each energy level of the at least two energy levels; performing material decomposition into at least two materials, one material of the two materials being iron, using the attenuation coefficients determined, yielding at least a fraction of iron in the region of interest defined; and determining the at least one first characteristic value, at least one of as and from the fraction of iron yielded.

Abstract: A method is for producing an identification unit for identifying image artifacts automatically. In an embodiment, the method includes providing a learning processing apparatus; providing an initial identification unit; providing a first image data library including artifact reference acquisitions containing image artifacts; and training the identification unit using the image artifacts. An identification method is for identifying image artifacts automatically in an image acquisition. In an embodiment, the identification method includes: providing a trained identification unit; providing an image acquisition produced via a medical imaging system; inspecting the image acquisition for image artifacts by the identification unit; and labeling the ascertained image artifacts.

Abstract: A method is for noise reduction in image recordings. In an embodiment, the method includes providing an input image; de-noising the input image and producing a de-noised input image; and adapting noise texture of pixels of the de-noised input image via an adaptation method, noise amplitude of the de-noised input image being largely retained and the noise texture of the pixels of the de-noised input image being adapted to correspond largely to a defined noise texture. A corresponding device, a production method for an adaptation device, such an adaptation device, and a control facility and a computed tomography system are also disclosed.

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: 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: A method is disclosed for analyzing image data from a patient after a minimally invasive intervention on a lesion, in particular after a tumor ablation on a tumor, wherein a pre-intervention image dataset showing the lesion and an intra- and/or post-intervention, second image dataset showing the intervention region are analyzed. In at least one embodiment of the method, the method includes deriving attribute data describing attributes of the image data from the image datasets at least in part automatically, the attribute data being usable as input data to an artificial intelligence analysis algorithm trained using training data from other patients, and the training data being associated with a ground truth. The method further includes determining, from the attribute data, change information that describes the change in the lesion and/or in the intervention region as a result of the intervention.

Abstract: A method is disclosed for configuring a medical imaging device. In at least one embodiment, the method includes providing an input data set specifying a clinical task, the input data set including quantified image requirements; transferring the input data set to a server; providing a configuration data set by the server depending on the input data set; transferring the configuration data set from the server to the medical imaging device within a defined time interval; and configuring the medical imaging device based on the configuration data set.

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: 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: 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 determining at least one first characteristic value of blood in a patient. In an embodiment, the method includes acquiring, via a computed tomography device, computed tomography data of the patient for at least two energy levels of radiation using multi-energy computed tomography; defining a region of interest including blood, in at least one image data set reconstructed from the computed tomography data acquired; determining, at least in the region of interest defined, attenuation coefficients for each energy level of the at least two energy levels; performing material decomposition into at least two materials, one material of the two materials being iron, using the attenuation coefficients determined, yielding at least a fraction of iron in the region of interest defined; and determining the at least one first characteristic value, at least one of as and from the fraction of iron yielded.

Abstract: An X-ray imaging method is for generating contrast-enhanced image data relating to an examination region of an object to be examined. In an embodiment of the method, first contrast-agent influenced measured X-ray projection data with a first X-ray energy spectrum and at least one set of second contrast-agent influenced measured X-ray projection data with a second X-ray energy spectrum are acquired from the examination region. Subsequently, image data assigned to a third X-ray energy spectrum with a third mean energy, based on the first and at least second measured X-ray projection data is reconstructed based on the first and at least second measured X-ray projection data that has been acquired. A mean energy of the first X-ray energy spectrum and a mean energy of the second X-ray energy spectrum are selected as a function of a dimension parameter value of the object that is to be examined.

Abstract: In a method and computer for determining an imaging parameter for an imaging procedure, a patient-specific imaging value for imaging an image data set is provided to a computer. An imaging parameter is determined in the computer by applying a trained imaging rule to the patient-specific imaging value. The trained imaging rule is based on a number of training data sets, wherein the training data sets each includes at least one patient-specific training imaging value and at least one training imaging parameter and at least one training quality evaluation. The complex influences of the patient-specific imaging values and the imaging parameters on the result of imaging can be quantified thereby. Allocation of the training quality evaluation by individual operators or specific groups of operators makes it possible to adjust the image recording parameters flexibly and individually. The imaging parameter can be determined such that the result of imaging is individually adjusted to an operator.

Abstract: A method is for local improvement of the image quality of an imaging X-ray acquisition. In an embodiment, the method includes localizing an imaging region of a structure; selecting an environment of the imaging region; setting an acquisition parameter for the environment of the imaging region, wherein the acquisition parameter for the environment of the imaging region is different from a region outside of the environment of the imaging region; and acquiring an image dataset.

Abstract: A method is for controlling an x-ray imaging device, in particular a computed tomography device. The x-ray imaging device includes an x-ray source and a photon counting detector as an x-ray detector. The methods includes, for an image acquisition process of a patient: determining at least one input parameter relating to at least one of an attenuation property of the patient and a purpose of the image acquisition; at least one of determining or adapting at least one operation parameter of the x-ray detector, dependent upon the at least one input parameter determined; and performing the image acquisition using the at least one operation parameter determined or adapted.

Abstract: A method is for producing an identification unit for identifying image artifacts automatically. In an embodiment, the method includes providing a learning processing apparatus; providing an initial identification unit; providing a first image data library including artifact reference acquisitions containing image artifacts; and training the identification unit using the image artifacts. An identification method is for identifying image artifacts automatically in an image acquisition. In an embodiment, the identification method includes: providing a trained identification unit; providing an image acquisition produced via a medical imaging system; inspecting the image acquisition for image artifacts by the identification unit; and labeling the ascertained image artifacts.