Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Healthineers Ag

Boehringer Ingelheim Vetmedica Gmbh

Siemens Healthcare Diagnostics Products Gmbh

Sebastian Schmidt

Rainer Alfred Kuth

Arne Hengerer

Björn Heismann

David Grodzki

Diana Martin

Markus Vester

Ralf Ladebeck

Bjoern Heismann

Robert Krieg

Ralph Oppelt

Walter Gumbrecht

Klaus Abraham-Fuchs

Thomas Ehben

Stefan Assmann

Philipp Hoelzer

Markus Schmidt

Michael Szimtenings

Thorsten Feiweier

Wolfgang Renz

Rainer Graumann

Martin Requardt

Stefan Roell

Stephan Biber

Michael Maschke

Bernhard Schmidt

Matthias Fenchel

Patrick Gross

Peter Paulicka

Grzegorz Soza

Manfred Stanzel

Johannes Reinschke

Volker Matschl

Carsten Thierfelder

Günther Platsch

Anke Weissenborn

Reto Merges

Kristin Schmiedehausen

Wolfgang Strob

Elena Nioutsikou

Annemarie Hausotte

Christian Zilch

Michael Kaus

Sabine Schaeffer-Kundler

Kera Westphal

Marion Hellinger

Jörn Mosner

Nico Kaiser

Bernhard Krauss

Joerg Roland

Bernd Schweizer

Gudrun Zahlmann

Edgar Müller

Karsten Hiltawsky

Bernhard Roas

Christian Geppert

Kirstin Jattke

Ralph Markus Wirtz

Peter Huber

Jonathan Sperl

Okan Ekinci

Mark-Aleksi Keller-Reichenbecher

Bernd Hofmann

Andreas Grimme

Rudolf Röckelein

Christian P Schultz

Clemens Bulitta

Philipp Hölzer

Holger Dresel

Jochem Knoche

Jan Ruff

Carsten Warmuth

Stefan Förtsch

Thomas Gossler

Kerstin Mueller

Walter Märzendorfer

Matthias Lichy

Susanne Schmolke

Xavier Battle

Norbert Gläsel

Ludwig Herbst

Katharina Socher

Jürgen Kampmeier

Ralph Strecker

Jeanette Lenger

Axel Heitland

Stefan Assman

Anke Siebert

Sven Meyburg

Sonja Boehmer-Lasthaus

Stefan Zwick

Fabian Kiessling

Yufan Zhao

Richard Frank

Philipp HÖLZER

A method is for supporting medical personnel. The method includes taking account of patient data of the patient in an electronic health record stored in a first database; determining at least one prevalence value, each relating to a diagnosis to be made for a group of people including the patient, to which the patient has been classified as belonging in dependence on at least one part of the patient data; generating, from the at least one prevalence value, at least one item of evaluation information based on at least one future examination procedure performable with respect to the diagnosis to be made, taking into account prediction information present in a second database describing at least one of sensitivity and specificity for the at least one future examination procedure with respect to the diagnosis to be made; and outputting the at least one item of evaluation information to the medical personnel.

Method for supporting medical personnel, support system, computer program and data carrier

A system and method for identifying abnormal medical images. The system can be configured to receive a medical image, segment an anatomical structure from the medical image to define a segmented dataset, register the segmented dataset to a baseline dataset defining a normal anatomical structure, classify, by an abnormality classifier, whether the anatomical structure within the medical image as either abnormal or normal, wherein the abnormality classifier comprises a machine learning algorithm trained to distinguish between normal and abnormal versions of the anatomical structure in medical images, and based on whether the anatomical structure can be segmented from the medical image, whether the segmented dataset can be registered to the baseline dataset, or a classification associated with the medical image output by the abnormality classifier, flagging the medical image as either normal or abnormal.

AI-based image analysis for the detection of normal images

An analysis method is for automatically determining radiological result data from radiology data sets. In an embodiment, the method includes provisioning a first radiology data set at least based on X-ray data of a first X-ray energy spectrum; provisioning at least one second radiology data set at least based on X-ray data of a second X-ray energy spectrum; provisioning an analysis unit including a neural network, to analyze radiology data sets including an input layer with a plurality of cells, a number of intermediate layers and an output layer representing a radiological result; analyzing the first radiology data set and the at least one second radiology data set, at least subsets of the first radiology data set and the at least one second radiology data set being assigned to different cells of the input layer for joint processing in the neural network; and acquiring result data on the output layer.

Analysis method and analysis unit for determining radiological result data

In a method for synchronous magnetic resonance (MR) imaging and radiation therapy using a combined system having a radiation apparatus and an MR imaging apparatus, wherein the system is designed to record MR signals of a subject during irradiation of the subject, a three-dimensional MR volume data set of the subject is acquired that contains a target volume for the irradiation, the location of a central beam of the radiation therapy apparatus relative to the subject, is continuously determined, and a second MR image data set is determined, which is positioned essentially perpendicular to the central beam. If the location of the central beam changes, a correspondingly changed second MR image data set is determined perpendicular to the central beam.

Method and magnetic resonance apparatus for synchronous imaging and radiation therapy

A method is for providing respiratory information. In an embodiment, the method includes receiving imaging data relating to a lung; calculating a perfusion fraction for each respective region of a set of regions of the lung, based on the imaging data; calculating a respective ventilation value for each respective region of the set of regions of the lung based on the imaging data; calculating a weighted average of respective ventilation values across all respective regions of the set of regions of the lung, wherein for each respective region of the set of regions of the lung, the respective ventilation value of the respective region is weighted with the perfusion fraction of the respective region; generating the respiratory information based on the weighted average of the respective ventilation values; and providing the respiratory information.

Method and data processing system for providing respiratory information

A method is for providing decision-supporting data. In an embodiment, the method includes receiving photon-counting computed tomography data relating to an examination region; determining a location of a thrombus in the examination region, based on the photon-counting computed tomography data received; generating the decision-supporting data, relating to at least one of the thrombus and a vascular wall in a region of the thrombus, based on the photon-counting computed tomography data received and the location of the thrombus determined; and providing the decision-supporting data generated.

Method and data processing system for providing decision-supporting data

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.

Method and system for determining sufficiency of measurement data for post-processing process

A method and a device are for detecting an anatomical feature of a section of a blood vessel. In an embodiment, the device includes a first interface embodied to receive a medical image data record, including a region of an anatomy of a patient to be examined, which has the section of a blood vessel; a first computing unit, embodied to apply a first algorithm pretrained on an anatomical feature for detecting the anatomical feature based upon the medical image data record; and a second interface embodied to provide an item of information based upon the detection of the anatomical feature. The anatomical feature includes a change in caliber of a blood vessel. Further, the presence of the anatomical feature is indicative of vascular pulmonary disease, in particular of chronic thromboembolic pulmonary hypertension.

Method and device for detecting an anatomical feature of a section of a blood vessel

A method is for classifying a risk for thrombus formation in an organ, in particular during an atrial fibrillation. In an embodiment, the method includes providing a medical image data set via a medical imaging device; calculating a flow parameter based on the medical image data set; assigning a risk parameter based on the flow parameter calculated; and&#x2014;providing the risk parameter.

Method for classifying a risk for thrombus formation in an organ, system for classifying a risk for thrombus formation in an organ, a computer program product and a computer readable medium

A first image data set of a patient is provided to a computer, and the computer performs a first analysis of the first image data set using a first analysis algorithm that is designed for an analysis purpose. After completion of the first analysis, a second analysis algorithm is added to a database to which the computer has access, so that the second analysis algorithm is now available for access by the computer. The second analysis algorithm is designed for the same purpose as the first analysis algorithm. After the second analysis algorithm has been added to the database, a second image dataset of the patient is provided to the computer. Even though the second analysis algorithm is now available to the computer for the same analysis purpose as the first analysis algorithm, the computer automatically defaults to use the first analysis algorithm to perform a second analysis of the second image data set. This helps to ensure that the first analysis and the second analysis are comparable to each other, because they have been performed by the same analysis algorithm.

Method and computer system for analyzing image data of a patient

A method is disclosed for providing a virtual tomographic stroke follow-up examination image. In an embodiment, the method includes: receiving a sequence of temporally successive tomographic perfusion imaging data sets of a region for examination; calculating the virtual tomographic stroke follow-up examination image of the region for examination by applying a trained machine learning algorithm to the sequence of temporally successive tomographic perfusion imaging data sets received; and providing the virtual tomographic stroke follow-up examination image calculated.

Method and providing unit for providing a virtual tomographic stroke follow-up examination image

A method and device for determining a flow situation in a vessel are disclosed. According to an embodiment of the method, a first image data set containing image information relating to the vessel is used and a vascular tree of the vessel is segmented based upon the first image data set. An organ is also segmented based upon the first image data set or of a second image data set and the organ is assigned at least one area of a parenchyma of the organ. Via texture analysis, a texture of the area of the organ is determined and, depending on the texture, the area of the organ is assigned a flow characteristic. Depending on the vascular tree and the flow characteristic assigned to the area of the organ, a value of a measured variable characteristic of the flow situation within the vessel is then determined via a numerical method.

Method and device for determining a flow situation in a vessel

A method for merging a three-dimensional analysis data record of a tissue sample of a patient with a three-dimensional image data record of the patient indicating, prior to removal, the removal area of the tissue sample. The method includes registering a three-dimensional intermediate data record of the three-dimensional analysis data record. The registering includes transforming analysis data of the three-dimensional analysis data record into intermediate data, corresponding to image data recorded with a modality with which the three-dimensional image data record has been recorded and including a resolution reduced, to determine the three-dimensional intermediate data record corresponding, in terms of resolution, to the image data record; and registering the three-dimensional intermediate data record with the three-dimensional image data record. Finally, the method includes merging the three-dimensional analysis data record and the three-dimensional image data record using a registration rule obtained during the registering of the three-dimensional intermediate data record.

Method for merging an analysis data record with an image data record, positioning device, computer program and electronically readable data storage medium

A method is disclosed for providing an optimized energy bin parameter set for photon-counting spectral computed tomography. In one embodiment, the method includes receiving photon-counting spectral computed tomography data related to a plurality of energy bins and an initial energy bin parameter set; and performing iteration steps of a plurality of iteration steps. An input of the first iteration step of the plurality of iteration steps includes the initial energy bin parameter set as an input energy bin parameter set and the input of each further iteration step of the plurality of iteration steps includes an adjusted energy bin parameter set calculated in the preceding iteration step of the plurality of iteration steps as the input energy bin parameter set.

Method and providing unit for providing an optimized energy bin parameter set for photon-counting spectral computed tomography

A method is for setting a contrast of a multi-energy CT image representation. In an embodiment of the method, a plurality of multispectral image datasets of an examination region of a patient are received. Furthermore, an automated identification of a structure to be examined is achieved based upon the multispectral image datasets and an automated combining of the differently weighted multispectral image datasets is accomplished such that the contrast between the structure to be examined and its environment is improved compared to a uniform weighting. Further, a multi-energy CT imaging method is described. An image setting device is also described. In addition, a computed tomography system is described.

Contrast-enhanced reproduction of spectral CT image data

Growing community of inventors

Weisendorf, Germany

Sebastian Schmidt