Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Medical Solutions USA, Inc.

Siemens Healthineers Ag

Klaus J Kirchberg

Vivek Kumar Singh

Terrence Chen

Yao-Jen Chang

Christine Lorenz

Ruhan Sa

Ankur Kapoor

Birgi Tamersoy

Andreas Wimmer

Julien Christian Barbot

Jan Boese

Peter Speier

Rui Liao

Günter Lauritsch

Chenyang Xu

Kai Ma

Christoph Guetter

Yanghai Tsin

Sunil Goraksha Patil

Corinna Maier

Steven Michael Shea

Santosh Pai

Patient biographic data may be estimated by receiving patient image data, applying the patient image data to a machine learned model, the machine learned model trained on second patient data and trained to map the second patient data to associated biographic data using machine learned features, generating the patient biographic data based on the applying and the machine learned features, and outputting the patient biographic data. The patient biographic data may include a patient weight, a patient height, a patient gender, and a patient age.

Estimating patient biographic data parameters

For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.

Patient weight estimation from surface data using a patient model

For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

Patient model estimation from camera stream in medicine

A method of deriving one or more medical scene model characteristics for use by one or more software applications is disclosed. The method includes receiving one or more sensor data streams. Each sensor data stream of the one or more sensor data steams includes position information relating to a medical scene. A medical scene model including a three-dimensional representation of a state of the medical scene is dynamically updated based on the one or more sensor data streams. Based on the medical scene model, the one or more medical scene model characteristics are derived.

Medical scene model

A method for training a learning-based medical scanner including (a) obtaining training data from demonstrations of scanning sequences, and (b) learning the medical scanner's control policies using deep reinforcement learning framework based on the training data.

Method and system for learning to obtain medical scans of patients

A method for device visualization includes receiving a set of physical characteristics including a description of spatial relationships of a plurality of markers within a device. Radiographic data of the device within a subject is acquired. An approximate location of each of the plurality of markers is identified within the radiographic data. A trajectory function is constructed for the device within the subject based on the identified approximate locations of each of the markers and the received set of physical characteristics. A section function is constructed for the device based on the set of physical characteristics and a 3D model is generated for the device based on the constructed trajectory function and the section function. A rendering of the 3D model is displayed on a display device.

Active catheter reconstruction for interventional magnetic resonance imaging

Localization of a coil is provided for magnetic resonance (MR)-guided intervention. A multi-scale decomposition and characteristic transitions in the power spectra for the coil are used to determine a distribution of likelihood of the coil being at each of various locations and/or to determine a confidence in the position determination. For example, the power spectra along each axis is used to generate a likelihood distribution of the location of the coil. The power spectra are decomposited at different scales. For each scale, the modulus maxima reflecting transitions in the power spectra are matched using various criteria. A likelihood is calculated for each of the matched candidates from characterizations of the matched candidates. The likelihood distribution is determined from a combination of the likelihoods from the various scales.

Localization of a device for MR-guided intervention

In a method of real-time 3-D visualization and navigation for interventional procedures a position is determined at which data will be acquired, and data is acquired at the position. Features are extracted from the acquired data. A model described by the extracted features is built or updated, wherein updating the model includes calculating at least one acquisition parameter of a set of acquisition parameters. 3-D scenes showing the interior or exterior of one or more organs are displayed using the model or data extracted from the model. A region for updating is determined.

Real-time virtual endoscopy

Automatic measurement of morphometric and motion parameters of a coronary target includes extracting reference frames from input data of a coronary target at different phases of a cardiac cycle, extracting a three-dimensional centerline model for each phase of the cardiac cycle based on the references frames and projection matrices of the coronary target, tracking a motion of the coronary target through the phases based on the three-dimensional centerline models, and determining a measurement of morphologic and motion parameters of the coronary target based on the motion.

Automatic measurement of morphometric and motion parameters of the coronary tree from a rotational X-ray sequence

A computer implemented method for automatically updating a geometric model of an object of interest includes determining, automatically, a region of the geometric model for updating according to a update parameter, acquiring data at the region, extracting features from the acquired data, updating the geometric model described by the extracted features, wherein updating the model includes calculating at least one acquisition parameter of a set of acquisition parameters, and displaying a scene showing the object of interest using the model.

Automatically updating a geometric model

Apparatus for remotely controlling parameters of an image scanning apparatus includes a software interface for translating commands from an external application for providing scanner control commands to a scanner control machine for control of the parameters; and the software interface includes syntax software for translating the commands from the external application into a given syntax for providing the scanner control commands.

Apparatus and method for remotely controlling in real time measurement parameters of a magnetic resonance (MR) scanner

Progress monitoring of lesions is done automatically by segmentation and registration of lesions in multi-phase medical images. A parametric level-set framework includes a model optimization for any number of lesions. The user specifies lesions in a baseline volume by clicking inside of them. The apparatus segments the lesions automatically in the baseline and follow-up volumes. The segmentation optimization compensates for lesion motion between baseline and follow-up volumes. 2D and 3D medical patient data can be processed by the methods.

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Plainsboro, NJ, United States of America

Klaus J Kirchberg