Abstract: A method for ascertaining at least one of a position or an orientation of an MR local coil unit for arrangement inside a main magnetic field includes providing a first 3D relative position of a reference sensor system in relation to the main magnetic field; receiving an acceleration vector from at least one acceleration sensor; retrieving a distance vector describing a fixed relative position as a function of the received acceleration vector; calculating a second 3D relative position of the at least one acceleration sensor in relation to the main magnetic field based on the first 3D relative position and the retrieved distance vector; and ascertaining the at least one of the position or the orientation of the MR local coil unit using the first 3D relative position and the second 3D relative position.

Abstract: For training a machine learning system for representing a patient body a plurality of stored medical imaging data sets each representing at least a part of a respective patient are obtained. A first one of the plurality of stored medical imaging data sets represents a different part of the patient body than a second one of the plurality of stored medical imaging data sets. A plurality of landmarks in the stored medical imaging data sets are estimated, and each of the stored medical imaging data sets are aligned to a predefined pose using the plurality of landmarks. A plurality of points in the aligned medical imaging data sets are sampled, and the machine learning system is trained based on at least the plurality of points. The learned parameters of the machine learning system are then stored and used in a method for inferring a body representation.

Abstract: A storage medium, computer program product, data server, imaging device, telemedicine system and method are for providing at least one image dataset. In an embodiment, the method includes processing at least one raw image dataset, acquired from a patient by an imaging device of a mobile unit, to create a processed image dataset; generating a reduced image dataset by reducing an amount of data of the processed image dataset; and storing at least one of the at least one raw image dataset, the processed image dataset and the reduced image dataset on a data server, the data server being part of the mobile unit and being connected to a network.

Abstract: For reconstruction of an image in MRI, unsupervised training (i.e., data-driven) based on a scan of a given patient is used to reconstruct model parameters, such as estimating values of a contrast model and a motion model based on fit of images generated by the models for different readouts and times. The models and the estimated values from the scan-specific unsupervised training are then used to generate the patient image for that scan. This may avoid artifacts from binning different readouts together while allowing for scan sequences using multiple readouts.

Abstract: A magnetic resonance imaging (MRI) system includes an internet protocol (IP) communication system for communicating IP-based communication data, an audio/video output, and an audio/video source. The MRI system also includes an audio/video/IP gateway device with multiple non-IP interfaces operable to receive and output audio/video data. The non-IP interfaces use non-IP protocols, while an IP interface connected to the communication system uses a predefined audio/video IP protocol. A controller is operable to convert received audio/video data between the non-IP and IP protocols, output converted audio/video data via the IP interface in IP protocol, and output converted audio/video data via the non-IP interfaces using the corresponding non-IP protocol.

Abstract: One or more example embodiments relates to a stretcher board of a patient couch. The stretcher board is configured to support a patient. The stretcher board includes a hollow body with a closed monolithic lateral area, the closed monolithic lateral area including an upwardly directed subregion defining a patient support surface, the patient support surface having an upwardly directed convex curvature.

Abstract: A computer-implemented method is for evaluating a CT data set of a coronary region of a patient regarding perivascular tissue of at least one target blood vessel. In an embodiment, the computer-implemented method automatically determines a centerline of at least the at least one target blood vessel in the CT data set; defines a region of interest as including at least one defined outer region radius around an extension interval of the centerline, the outer region radius being relatively larger than a radius of the at least one target blood vessel; and calculates at least one quantitative value from CT data in the region of interest.

Abstract: One or more example embodiments relates to a computer-implemented method for determining a similarity measure, the similarity measure describing a similarity between a first patient and a second patient. The method includes receiving a first patient data record, wherein the first patient data record is assigned to the first patient; receiving a second patient data record, wherein the second patient data record is assigned to the second patient; receiving or determining a medical ontology, wherein the medical ontology is independent of the first patient data record and the second patient data record; determining a patient ontology based on the medical ontology and at least one of the first patient data record or the second patient data record; and determining the similarity measure based on the patient ontology.

Abstract: A gantry drive includes a multiphase stator, a multiphase rotor, a tunnel-shaped cavity configured to receive a patient, and a controller. The multiphase rotor spatially surrounds the cavity. The multiphase stator and the multiphase rotor form a multiphase motor configured to produce a rotating-field power. A first portion of the rotating-field power provides a mechanical drive power to produce a rotational movement of the multiphase rotor, and a second portion of the rotating-field power provides an electrical supply power for a payload apparatus. The controller is configured to vary at least one of the first portion or the second portion.

Abstract: A method is for checking a characteristic variable of an application procedure of an X-ray based medical imaging application, each patient model, of a plurality of patient models stored in a database, is characterized by at least one model parameter. In addition, a subset of the plurality of patient models is selected based upon the at least one model parameter assigned to a patient model or based upon the X-ray based medical imaging application. In addition, a number of simulated application procedures of the X-ray based imaging application are performed based upon the selected subset of patient models, at least one patient model being input into each performed simulated application procedure. Furthermore, a value of the characteristic variable is ascertained for each performed simulated application procedure. In addition, the value of the characteristic variable ascertained is output or is automatically evaluated based upon a specified target for the characteristic variable.

Abstract: A method is for creating a controller for controlling an output unit from information in the context of medical diagnostics and therapy. The method includes providing a learning processing apparatus designed via an algorithm to recognize spoken words; providing on, or in, the learning processing apparatus, an untrained controller, designed to be trained via machine learning; providing a number of speech recordings, each including a communication during a medical procedure, wherein the speech recordings concern comparable medical procedures; performing a speech analysis of the speech recordings; and training the untrained controller according to a machine learning principle based upon the speech analysis of the speech recordings.

Abstract: Systems and methods for predicting risk for a medical event associated with evaluating or treating a patient for a disease are provided. Input medical imaging data and patient data of a patient are received. The input medical imaging data includes abnormality patterns associated with a disease. Imaging features are extracted from the input medical imaging data using a trained machine learning based feature extraction network. One or more of the extracted imaging features are normalized. The one or more normalized extracted imaging features and the patient data are encoded into features using a trained machine learning based encoder network. Risk for a medical event associated with evaluating or treating the patient for the disease is predicted based on the encoded features.

Abstract: In accordance with a method for operating an MRT system a first MR recording is performed so as to map an object to generate first MR data that represents the object. The first MR recording is performed in accordance with a first k-space scanning scheme and during the first MR recording at least one first excitation pulse is transmitted. A second MR recording that is different from the first MR recording is performed to generate second MR data and a noise component is determined in dependence upon the second MR data by a computing unit and the noise component represents an influence of at least one external noise source. An MR image is generated by the computing unit in dependence upon the first MR data and in dependence upon the noise component.

Abstract: In a method and system for automatically positioning a region of interest of a patient for a medical imaging examination in an isocenter of a medical imaging apparatus, the region of interest of the patient is brought into a patient receiving region of the medical imaging examination for a position-determination measurement, a position-determination measurement is performed to capture position-determination image data, the position-determination image data is analyzed to determine a position of the region of interest of the patient from the position-determination image data, and the patient is automatically positioned such that the position of the region of interest of the patient coincides with the position of the isocenter of the medical imaging apparatus.

Abstract: Machine-based risk prediction or assistance is provided for peri-procedural complication, such as peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in PCI. Various combinations of types or modes of information are used in the prediction, such as both imaging and non-imaging data. The prediction may be made prior to, during, and/or after PCI using the machine-learned model to more quickly reduce the chance of PMI. The workflows for prior, during, and/or post PCI incorporate the risk prediction and/or risk-based recommendations to reduce PMI for patients.

Abstract: A couchtop board overlay for a patient couchtop board extends along a longitudinal direction from a first end section to a second end section. The couchtop board overlay includes a first countercoupling interface in the first end section, wherein, in a state coupled to the first coupling interface, the first countercoupling interface is configured to transmit an application of force in the longitudinal direction onto the couchtop board overlay. The couchtop board overlay further includes a second countercoupling interface in the second end section, wherein, in a coupled state, the second countercoupling interface is connected to the second coupling interface in at least one of a positive-locking or a force-fitted manner.

Abstract: A computer-implemented method for comparing follow-up medical findings includes receiving image data showing a body part of a patient at a first time and retrieving, from a database, reference data associated to the image data. The reference data includes reference biometric data and one or more reference medical findings. Thereby the reference biometric data and the one or more reference medical findings have been extracted from reference image data depicting the body part of the patient at a second time, different than the first time. The method further includes processing the received image data to extract, from the image data, biometric data corresponding to the reference biometric data and one or more medical findings; performing a registration of the extracted biometric data with the reference biometric data; and comparing the extracted medical findings with the reference medical findings using the registration.

Abstract: A calling device of a MRI system may include a reader/writer and a call maker. The reader/writer can send a fixed-frequency electromagnetic wave. The call maker may include a main body, an antenna, an operating member, and an answering controller. The antenna can generate an induced current after receiving the fixed-frequency electromagnetic wave. The operating member can switch between first and second positions relative to the main body. The answering controller is configured such that: when the operating member is in the first position, a first radio-frequency signal is sent through the antenna with energy obtained from the induced current generated by the antenna, and when the operating member is in the second position, a second radio-frequency signal is sent through the antenna with energy obtained from the induced current generated by the antenna. The reader/writer can send different prompt signals according to the first and second radio-frequency signals.

Abstract: The present disclosure is directed to techniques for synchronizing a rotational eccentric mass of a gravitational transducer used for a magnetic resonance elastography acquisition with a corresponding magnetic resonance elastography scan carried out by a magnetic resonance imaging system, wherein the rotation of the eccentric mass is driven by a shaft. The method includes starting the rotation of the eccentric mass at a set vibration frequency and the magnetic resonance elastography scan at a set acquisition frequency; determining the rotational position of the shaft; defining the rotational position as first reference position; calculating further reference positions. At the start time of each subsequent acquisition period, determining the current rotational position of the shaft; comparing the determined current rotational position with the theoretically expected reference position and decreasing or increasing the rotational speed of the rotational eccentric mass based on the comparison.

Abstract: A computer-implemented method for providing a vascular image data record includes receiving a plurality of projection-X-ray images recorded in temporal succession. The plurality of projection-X-ray images at least partially map a common examination region of an examination object. The plurality of projection-X-ray images map a temporal change in the examination region of the examination object. A change image data record is determined in each case based on at least one region of interest of the plurality of projection-X-ray images. The at least one region of interest includes a plurality of image points. The change image data record in each case includes a time-intensity curve for each of the image points. The vascular image data record is generated based on the change image data record, and the vascular image data record is provided.