Siemens Healthcare Gmbh

Siemens Healthineers Ag

Philipp Hoelzer

Grzegorz Soza

Razvan Ionasec

Mark-Aleksi Keller-Reichenbecher

Sebastian Schmidt

Bernhard Schmidt

Sasa Grbic

Christian Hofmann

Thilo Hannemann

Stefan Reichelt

Christopher Jude Amies

Thomas Boettger

Bjoern Kreisler

Andre Ritter

Rene Kartmann

Gerhard Kraemer

Christophe Della Monta

Michael Suehling

Xavier Battle

Dorin Comaniciu

Bogdan Georgescu

David Grodzki

Thomas Flohr

Tommaso Mansi

Rainer Alfred Kuth

Ingmar Voigt

Michael Grasruck

Daguang Xu

Helene C Houle

Charles Henri Florin

Rainer Grimmer

Axel Heitland

Nico Kaiser

Peng Hao

Computed tomography (CT) measurement data of the patient is acquired using a CT device having a quantum counting X-ray detector, and the CT measurement data is processed to generate result data, considering a specific information content of the CT measurement data resulting from the use of the quantum counting X-ray detector in acquiring the CT measurement data. The result data is suitable for use in the planning of irradiation of the patient. The result data is provisioned to an interface such that the result data is usable for planning the irradiation of the patient.

Method for providing result data which is suitable for use in planning the irradiation of a patient

A method is for adapting method steps for finding a result in a CT-based decision support method to the evaluation of LDCT image datasets. In an embodiment of the method, a plurality of reference image datasets are acquired from a plurality of patients. A reference image dataset features at least one CT image dataset from one of the plurality of patients and an LDCT dataset from the patient. Furthermore, method steps for establishing result data are applied to the different image datasets of the reference image datasets. The result data is compared with one another and the method steps for establishing result data are adapted based upon a result of the comparison to the establishing of result data with reference to an LDCT image dataset. An LDCT-based decision support method is also described. Moreover an adaptation device is described. A system for LDCT-based decision support is further described.

Flexible use of a clinical decision support system

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.

Method and system for outputting augmented reality information

The user is to be informed of the reliability of the machine-learned model based on the current input relative to the training data used to train the model or the model itself. In a medical situation, the data for a current patient is compared to the training data used to train a prediction model and/or to a decision function of the prediction model. The comparison indicates the training content relative to the current patient, so provides a user with information on the reliability of the prediction for the current situation. The indication deals with the variation of the data of the current patient from the training data or relative to the prediction model, allowing the user to see how well trained the predication model is relative to the current patient. This indication is in addition to any global confidence output through application of the prediction model to the data of the current patient.

Characterization of amount of training for an input to a machine-learned network

A method for setting a medical imaging protocol includes providing an information data set assigned to a patient. The information data set includes an information about a provisional diagnostic finding regarding the patient. In an embodiment, the method further includes assigning a probability value for a positive finding of the provisional diagnostic finding to the information data set; and automatically setting the medical imaging protocol. The medical imaging protocol is adapted to the provisional diagnostic finding such that an analysis of a result of the medical imaging protocol changes the probability value.

Method for setting a medical imaging protocol, system for setting a medical instrument, computer program and computer-readable medium

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 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

In personalized object creation, for implants, medical imaging is used to derive a model personalized to a patient. The model may be of a dynamic structure, such as part of the cardiovascular system, and is used to print the implant itself. The model may be used to print a mold to create the implant, a scaffold on which to grow tissue, and/or tissue itself. In another or additional approach, the medical imaging information is used to determine tissue properties. Differences in a material property of the anatomy is mapped to different materials used by a multi-material 3D printer, resulting in a printed object reflecting the size, shape, and/or other material property of the anatomy of the patient.

Personalized creation from medical imaging

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 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.

Computed tomography recording with different sets of energy thresholds

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.

Classification by means of recordings

A method includes provisioning a set of training data sets, each of the training data sets respectively including an acquisition data set; generating a first medical image for each of the training data sets of at least one first subset of the set of training data sets using the image reconstruction algorithm based on a respective acquisition data set; determining an image processing result for each of the respective first medical images using an image processing algorithm based on the respective first medical image; determining image processing information for each of the respective first medical images relating to a quality of the respective image processing result based on the respective image processing result; and optimizing the image reconstruction algorithm based on a first machine learning algorithm, the at least one first subset of the set of training data sets and the image processing information for the respective first medical images.

Method and data processing unit for optimizing an image reconstruction algorithm

Growing community of inventors

Bubenreuth, Germany

Philipp Hoelzer