Abstract: A method is for controlling a medical imaging system. The method includes providing a data record for a patient, the data record including indication data; mapping at least the indication data into an ontology; providing a control data library including control data records for at least one medical imaging system; automatically determining a control data record from the control data library as a function of the at least indication data mapped into the ontology, via at least one of an inference and numerical modeling; and controlling the medical imaging system with the control data record automatically determined. Furthermore, a related control facility and a medical imaging system are also disclosed.

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: A trained ML algorithm may be configured to process medical imaging data to generate a prediction of at least one diagnosis of a patient based on the medical imaging data. The prediction of the at least one diagnosis of the patient is compared with a validated label of the at least one diagnosis of the patient and the performance of the trained ML algorithm is determined based on the comparison. The validated label of the at least one diagnosis of the patient is obtained by parsing a validated radiology report of the patient and the medical imaging data is associated with the validated radiology report. If the performance of the trained ML algorithm is lower than a threshold, an update of parameters of the trained ML algorithm may be triggered based on the validated label.

Abstract: A method and system are disclosed for outputting augmented reality information to a first user. In an embodiment, the method includes acquiring first information, including image information, depth information, coordinate information and combinations thereof, the first information relating to at least one of a medical device and a medical examination of a patient; creating the augmented reality information, relating to the medical device and/or the medical examination of the patient, based on the first information; and outputting the augmented reality information such that the augmented reality information is perceivable in a field of view of the first user.

Abstract: A method is for using measurement data of an object of examination for a post-processing process. In an embodiment, the method includes recording first measurement data, the first measurement data being previously determined via a medical imaging modality; automatically analyzing the first measurement data based on defined criteria and automatically inspecting a set of control parameters with aid of an analysis of the first measurement data using defined criteria with regard to second measurement data, the second measurement data being previously recorded via the modality using the set of control parameters, wherein the defined criteria include at least one of a post-processing capacity and identification of at least one image characteristic; and using at least one of the first measurement data and the second measurement data in a post-processing process. A control device and a medical imaging system are also disclosed.

Abstract: A method is for image data processing. In an embodiment, the method includes providing a 3D medical image data record, which relates to an elongated anatomical structure, a center line of the elongated anatomical structure being defined in the 3D medical image data record; defining at least one curved slice in the 3D medical image data record, the at least one curved slice winding around the center line; scanning at least one part of the 3D medical image data record into the at least one curved slice; and unrolling the at least one curved slice, into which the at least one part of the 3D medical image data record was scanned, at least one unrolled flat slice being determined. An image data processing unit is also for image data processing and a medical imaging apparatus includes the image data processing unit.

Abstract: Systems and methods are provided for automatic detection and quantification for traumatic bleeding. Image data is acquired using a full body dual energy CT scanner. A machine-learned network detects one or more bleeding areas on a bleeding map from the dual energy CT scan image data. A visualization is generated from the bleeding map. The predicted bleeding areas are quantified, and a risk value is generated. The visualization and risk value are presented to an operator.

Abstract: A method for supporting an evaluator in evaluation of a CT data set of a vascular system is provided. The vascular system is segmented, and an evaluation parameters are determined from the segmentation. An abstracted representation of the vascular system up to the limit generation is displayed, where each vascular segment is allocated at least one display element of a predefined minimum size that is the same for all vascular segments. Display elements of a path from the vascular segment of the zeroth generation to a vascular segment of the limit generation are represented in a first direction of the representation in succession, and display elements of the same generation allocated to different paths follow one another in a second direction transverse thereto. Each display element allocated to a vascular segment is represented in a type of representation corresponding to the value of the evaluation parameter for the vascular segment.

Abstract: A method for supporting an evaluator in evaluation of a CT data set of a vascular system is provided. The vascular system is segmented, and an evaluation parameters are determined from the segmentation. An abstracted representation of the vascular system up to the limit generation is displayed, where each vascular segment is allocated at least one display element of a predefined minimum size that is the same for all vascular segments. Display elements of a path from the vascular segment of the zeroth generation to a vascular segment of the limit generation are represented in a first direction of the representation in succession, and display elements of the same generation allocated to different paths follow one another in a second direction transverse thereto. Each display element allocated to a vascular segment is represented in a type of representation corresponding to the value of the evaluation parameter for the vascular segment.

Abstract: The invention relates to a method for imaging a three-dimensional object to be examined. According to said method, a three-dimensional parameterized area is determined which is in conformity with an anatomic structure of the three-dimensional object to be examined. The three-dimensional parameterized area is imaged onto a two-dimensional parameterized area. The three-dimensional object to be examined is represented by imaging pixels that are associated with the three-dimensional parameterized area onto the two-dimensional parameterized area. The invention further relates to a method for determining a camera position in a three-dimensional image recording of an object to be examined. The invention also relates to a method for representing a section of an object to be examined. The invention finally relates to a device for imaging a three-dimensional object to be examined.

Abstract: A method and system are disclosed for outputting augmented reality information to a first user. In an embodiment, the method includes acquiring first information, including image information, depth information, coordinate information and combinations thereof, the first information relating to at least one of a medical device and a medical examination of a patient; creating the augmented reality information, relating to the medical device and/or the medical examination of the patient, based on the first information; and outputting the augmented reality information such that the augmented reality information is perceivable in a field of view of the first user.

Abstract: A method and system are disclosed for outputting augmented reality information to a first user. In an embodiment, the method includes acquiring first information, including image information, depth information, coordinate information and combinations thereof, the first information relating to at least one of a medical device and a medical examination of a patient; creating the augmented reality information, relating to the medical device and/or the medical examination of the patient, based on the first information; and outputting the augmented reality information such that the augmented reality information is perceivable in a field of view of the first user.

Abstract: An adaptive method for generating CT image data is described. In the method, projection measurement data of an examination region of an examination object is acquired. Furthermore, uncorrected image data of the examination region is generated. Artifact-affected subregions of the examination region are determined on the basis of at least one part of the uncorrected image data. An artifact-reduced image reconstruction is carried out in the artifact-affected subregions of the examination region. Only artifact-reduced subimage data of the artifact-affected subregions is generated. Finally, artifact-reduced image data of the entire examination region is generated by combining at least one part of the uncorrected image data and the artifact-reduced subimage data. A reconstruction device is also described. Moreover, a computed tomography system is described.

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: A method is for classifying an examination object by way of recordings. In an embodiment, the method includes capturing at least one optical recording of the examination object; determining and quantifying a number of defined characteristics of the examination object based upon an analysis of the optical recordings with the aid of a machine learning method; and affecting the classification of the examination object in respect of a classification criterion, based upon the quantified characteristics with the aid of a machine learning method. Also described are a classification entity and a medical imaging modality.

Abstract: A method for automatically identifying a potential pleural effusion in medical image data of a thorax of a patient from a scan by means of a medical scanner is provided. It includes at least the steps of accepting rib cage detection data of the rib cage of the patient from the image data, which rib cage detection data include rib cage extent data of a rib cage extent of the interior of the rib cage, accepting lung detection data of the lung of the patient from the image data, which lung detection data comprise lung extent data of a lung extent of the external boundary of the lung, accepting mediastinum detection data of all organs of the mediastinum in the thorax (Th) of the patient from the image data, which mediastinum detection data comprise mediastinum extent data of a mediastinum extent of the external boundary of the mediastinum, and subtracting the lung extent and the mediastinum extent from the rib cage extent while forming pleural effusion identification data.

Abstract: A method for defining scanning parameters of a CT scan of a region of interest of an examination object (O) using a CT system is described. In an embodiment of the method, external image capture of external features of the examination object is carried out using an additional image capture unit. In addition, at least one scanning parameter in the axial direction of the CT system is determined on the basis of the external image capture. Finally, a CT scan is performed using the at least one scanning parameter determined. A CT scan range defining device is additionally described. A computed tomography system is also described.

Abstract: A method and system are disclosed for outputting augmented reality information to a first user. In an embodiment, the method includes acquiring first information, including image information, depth information, coordinate information and combinations thereof, the first information relating to at least one of a medical device and a medical examination of a patient; creating the augmented reality information, relating to the medical device and/or the medical examination of the patient, based on the first information; and outputting the augmented reality information such that the augmented reality information is perceivable in a field of view of the first user.

Abstract: A method and system are disclosed for outputting augmented reality information to a first user. In an embodiment, the method includes acquiring first information, including image information, depth information, coordinate information and combinations thereof, the first information relating to at least one of a medical device and a medical examination of a patient; creating the augmented reality information, relating to the medical device and/or the medical examination of the patient, based on the first information; and outputting the augmented reality information such that the augmented reality information is perceivable in a field of view of the first user.

Abstract: A method for estimating a body surface model of a patient includes: (a) segmenting, by a computer processor, three-dimensional sensor image data to isolate patient data from environmental data; (b) categorizing, by the computer processor, a body pose of the patient from the patient data using a first trained classifier; (c) parsing, by the computer processor, the patient data to an anatomical feature of the patient using a second trained classifier, wherein the parsing is based on a result of the categorizing; and (d) estimating, by the computer processor, the body surface model of the patient based on a result of the parsing. Systems for estimating a body surface model of a patient are described.