Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Energy, Inc.

Birgi Tamersoy

Vivek Kumar Singh

Terrence Chen

Yao-Jen Chang

Kai Ma

Ankur Kapoor

Ralf Nanke

Andreas Wimmer

Brian Teixeira

Andreas Krauss

Susanne Oepping

Ruhan Sa

Ziyan Wu

Klaus J Kirchberg

Yifan Wu

David Grodzki

Mariappan S Nadar

Boris Mailhe

Gerhard Kleinszig

Alexander Gemmel

ALois Regensburger

Jan Ernst

Thomas Pheiffer

Michael Fuhrmann

Amilcar Alzaga

Arnaud Arindra Adiyoso

Olivier Ecabert

Martin Kraus

Susanne Dornberger

Kevin P Bailey

Jiangping Wang

Andre Aichert

Tobias Heimann

Rui Wang

Marvin Teichmann

Jianping Wang

Andreas Krauß

Zhuokai Zhao

In magnetic resonance imaging, shape estimation is used to limit patient burns. A localizer image or scout scan is used to determine some of the patient shape and corresponding position. A missing part, such as the arm not in the scout scan field of view, is inferred from the localizer image. The position of the inferred body part is used to predict the risk of burn, allowing generation of a warning to reposition the patient and/or change the scan settings.

Body shape estimation from localizer scan in magnetic resonance medical imaging

A stream of virtual topograms, in particular live virtual topograms, is predicted. Sets of surface data of an outer surface of a subject are continuously received. Based on each received set of surface data a (live) virtual topogram is continuously generated by a trained machine learning algorithm (MLA). Thereto, a representation of body landmarks is updated based on each received set of surface data by a trained body marker detector (BMD), of the trained MLA, and the (live) virtual topogram is predicted based on the updated spatial marker map and on the corresponding set of surface data by a trained topogram generator (TG) of the trained MLA.

Method and systems for predicting a stream of virtual topograms

A model dataset is generated based on first image data. The model dataset and second image data map at least a common part of an examination region at a second detail level. The model dataset and the second image data are pre-aligned at a first detail level below the second detail level based on first features that are mapped at the first detail level in the model dataset and the second image data and/or an acquisition geometry of the second image data. The model dataset and the second image data are registered at the second detail level based on second features that are mapped at the second detail level in the model dataset and the second image data. The second class of features is mappable at the second detail level or above. The registered second image data and/or the registered model dataset is provided.

Providing result image data

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.

Body representations

Object specific in-homogeneities in an MRI system are corrected. Prescan information available at the MR imaging system is determined. The prescan information includes at least object specific information of an object located in the MR imaging system from which an MR image is to be generated. The prescan information does not include a B1 map of the MRI system with the object being present in the MR imaging system. The prescan information is applied to a trained machine learning module provided at the MRI system. The trained machine learning module determines and generates shimming information as output. The shimming information is applied to a shimming module of the MR imaging system, wherein the shimming module uses the shimming information to generate a corrected magnetic field B0.

Method for correcting object specific inhomogeneities in an MR imaging system

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

Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.

Digital pathology using an artificial neural network

For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.

Multi-task progressive networks for patient modeling for medical scans

A method for controlling a scanner comprises: sensing an outer surface of a body of a subject to collect body surface data, using machine learning to predict a surface of an internal organ of the subject based on the body surface data, and controlling the scanner based on the predicted surface of the internal organ.

Method for controlling scanner by estimating patient internal anatomical structures from surface data using body-surface and organ-surface latent variables

Synthetic CT is estimated for planning or other purposes from surface data (e.g., depth camera information). The estimation uses parameterization, such as landmark and/or segmentation information, in addition to the surface data. In training and/or application, the parameterization may be used to correct the predicted CT volume. The CT volume may be predicted as a sub-part of the patient, such as estimating the CT volume for scanning one system, organ, or type of tissue separately from other system, organ, or type of tissue.

Synthetic parameterized computed tomography from surface data in medical imaging

For topogram predication from surface data, a sensor captures the outside surface of a patient. A generative adversarial network (GAN) generates the topogram representing an interior organ based on the outside surface of the patient. To further adapt to specific patients, internal landmarks are used in the topogram prediction. The topogram generated by one generator of the GAN may be altered based on landmarks generated by another generator.

Topogram prediction from surface data in medical imaging

A method is disclosed for adjusting a collimator of an X-ray source. In an embodiment, the method includes detecting an arrangement of an X-ray detector with respect to the X-ray source; automatically determining an adjustment for the collimator based on the detected position of the X-ray detector with respect to the X-ray source; and automatically adjusting the collimator based on the determined adjustment for the collimator. An X-ray device and computer readable medium are also disclosed.

Adjusting a collimator of an X-ray source

During the generation of a panoramic x-ray recording, the use of semi-transparent x-ray screens allows the patient's x-ray exposure to be reduced when partial x-ray images are created, in spite of relatively large overlapping areas between the partial x-ray images.

Device and method for creating a panoramic x-ray recording using a first semi-transparent x-ray screen and a second semi-transparent x-ray screen

For anomaly detection based on topogram predication from surface data, a sensor captures the outside surface of a patient. A generative adversarial network (GAN) generates a topogram representing an interior anatomy based on the outside surface of the patient. An X-ray image of the patient is acquired and compared to the generated topogram. By quantifying the difference between the real X-ray image and the predicted one, anatomical anomalies may be detected.

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Erlangen, Germany

Birgi Tamersoy