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 for reconstructing image data during CT imaging is described. In the method, a plurality of independent data records of projection measured data are captured. A combined data record is then determined based on the captured data records. In addition, morphological information is determined based on the combined data record. A target data record is also determined based on the captured independent data records. A target image data record is reconstructed based on the target data record and the determined morphological information. An image data determination facility and a computed tomography system are also described.

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 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 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: A computed tomography (CT)-based clinical decision support system provides fractional flow reserve (FFR) decision support. The available data, such as the coronary CT data, is used to determine whether to dedicate resources to CT-FFR for a specific patient. A machine-learnt predictor or other model, with access to determinative patient information, is used to assist in a clinical decision regarding CT-FFR. This determination may be made prior to review by a radiologist and/or treating physician to assist decision making.

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: A CT-based clinical decision support system provides coronary decision support. With or without CT-FFR, a machine learnt predictor predicts the clinical decision for the patient based on input from various sources. Using the machine learnt predictor provides more consistent and comprehensive consideration of the available information. The clinical decision support may be provided prior to review of coronary CT data by a radiologist and/or treating physician, providing a starting point or recommendation that may be used by the radiologist and/or treating physician.

Abstract: A method is for imaging an examination region of an object to be examined, the examination region including a first contrast medium and a second contrast medium different from the first contrast medium. In an embodiment, the method includes acquisition and generation. In the acquisition, first projection scan data is acquired in an examination region with a first energy range and second projection scan data, different from the first projection scan data, is acquired with a second energy range different from the first energy range. In the generation, a first image is generated based upon the first projection scan data and the second projection scan data and the first image has furthermore only isolated first information of the first contrast medium, only isolated second information of the second contrast medium, or isolated first information of the first contrast medium together with isolated second information of the second contrast medium.

Abstract: A computed tomography (CT)-based clinical decision support system provides fractional flow reserve (FFR) decision support. The available data, such as the coronary CT data, is used to determine whether to dedicate resources to CT-FFR for a specific patient. A machine-learnt predictor or other model, with access to determinative patient information, is used to assist in a clinical decision regarding CT-FFR. This determination may be made prior to review by a radiologist and/or treating physician to assist decision making.

Abstract: A CT-based clinical decision support system provides coronary decision support. With or without CT-FFR, a machine learnt predictor predicts the clinical decision for the patient based on input from various sources. Using the machine learnt predictor provides more consistent and comprehensive consideration of the available information. The clinical decision support may be provided prior to review of coronary CT data by a radiologist and/or treating physician, providing a starting point or recommendation that may be used by the radiologist and/or treating physician.

Abstract: A method is for processing a first image data set including a first image value tuple associated with a volume element of a region of an object to be imaged. In an embodiment, a second image data set is generated based upon the first image data set, including a second image value tuple associated with the volume element, a base material decomposition being capable of being carried based upon the second image data set and based upon a base material set; a starting area and a target area are selected as a function of the base material set, the first image value tuple being located in the starting area; the second image value tuple is ascertained based upon the first image value tuple, the second image value tuple being associated with the first image value tuple via image value tuple imaging and being located in the target area.

Abstract: A method is for the characterization of plaque in a region of interest inside an examination subject by way of a plurality of image data sets. The image data sets have been reconstructed from a plurality of projection data sets, which have been acquired via a CT device using different X-ray energy spectra. The method includes: acquiring the image data sets, which include a plurality of pixels. Spectral parameter values are acquired on a pixel by pixel basis using at least two image data sets. Character parameter values are then acquired on a pixel by pixel basis to characterize plaques on the basis of the spectral parameter values. An analysis unit and a computed tomography system are also disclosed.

Abstract: A method includes acquiring contrast medium-enhanced projection measurement data from the examination region, including at least two spectral projection measurement data sets. Further, image data is reconstructed based upon the acquired projection measurement data, the image data including at least two spectral image data sets. Subsequently, texture parameters are determined based upon the reconstructed image data and a parameter analysis is carried out based upon the parameter database. In addition, an image analysis apparatus and a computed tomography system are described.

Abstract: A method for creating a resultant image for a specifiable, virtual x-ray quanta energy distribution includes capturing a first image dataset of the patient, capturing at least one second image dataset of the patient, and specifying a virtual x-ray quanta energy distribution. The method also includes establishing a spatial density distribution of the patient for at least two materials based on the first image dataset and the at least one second image dataset. The method includes creating a third image dataset of the patient based on the specified virtual x-ray quanta energy distribution and the established spatial material density distributions. The third image dataset represents an x-ray attenuation distribution of the patient corresponding to the specified virtual x-ray quanta energy distribution. The method also includes creating the virtual image from the third image dataset.

Abstract: An example embodiment relates to a method for automatically stipulating a spectral distribution of the X-ray radiation of a number of X-ray sources of a computed tomography system. This involves firstly stipulating start control parameters of an X-ray source, which determine the dose and spectral distribution of X-ray radiation. On the basis of an expected attenuation of the X-ray radiation by an examination object, an examination-object-specific basic control parameter is then ascertained proceeding from the start control parameters. Afterwards, on the basis of the expected attenuation of the X-ray radiation by the examination object and the basic control parameter, first target control parameters and second target control parameters are ascertained for setting the spectral distribution of the X-ray radiation in a subsequent multi-energy measurement on the examination object. Moreover, a method, a control device suitable, and a computed tomography system comprising such a control device are described.

Abstract: A method is described for generating contrast medium-aided CT image data from an examination region of a patient. At least two sets of projection measurement data assigned to different X-ray energy spectra are acquired from the examination region. At least two different contrast media are present simultaneously during the acquisition in the examination region. Furthermore, at least two separate image datasets are reconstructed based upon the at least two sets of projection measurement data with the aid of a multi-material decomposition. Each of the at least two separate image datasets is assigned to one of the at least two contrast media and the materials according to which decomposition takes place are the respective contrast media. Additionally a method for analyzing morphological and/or functional parameters assigned to different contrast media in an examination region of a patient is described. Furthermore an image reconstruction device and a computed tomography system is described.

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 and an apparatus are disclosed for patient-dependent optimization and reduction of the contrast medium amount. An embodiment includes provisioning patient-specific data, containing information about a patient cross-section relevant for the imaging measurement; establishing CT values and/or CNR values of a contrast medium to be expected for at least one region of interest of the patient for different candidate settings of x-ray source arrangement and/or of an image reconstruction device; determining a relevant candidate contrast medium amount for the different candidate settings, which would lead to a CT value or CNR value of the contrast medium which corresponds to the CT value or CNR value of the contrast medium at the reference setting of the x-ray source arrangement and/or image reconstruction device; and displaying an optimum relative candidate contrast medium amount and of the associated candidate setting.

Abstract: A filter arrangement is described. The filter arrangement includes an inner filter region, covering a cross-sectional area of a fan beam of a first X-ray source, and an outer filter region covering, together with the inner filter region, a larger cross-sectional area of a larger fan beam of a second X-ray source. The inner filter region has a first spectral filter function. The outer filter region is divided into a first subregion having a second spectral filter function and a second subregion having a third different spectral filter function. A computed tomography system is described. A method for producing a filter arrangement for a spectral filtering of X-ray radiation of a CT system having a first X-ray source and a second X-ray source is also presented. A method for reconstructing image data on the basis of projection measurement data is also described.