Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Healthineers Ag

Other

Institut National De Recherche En Informatique Et En Automatique

Siemens Corporation

Johns Hopkins University

Yale University

Technische Universitat Munchen

Siemens Ag Osterreich

Inria

Corindus, Inc.

Tommaso Mansi

Dorin Comaniciu

Bogdan Georgescu

Ali Kamen

Ingmar Voigt

Tiziano Passerini

Shun P Miao

Sasa Grbic

Rui Liao

Viorel Mihalef

Saikiran Rapaka

Helene C Houle

Ankur Kapoor

Julian Krebs

Puneet Sharma

Shaohua Kevin Zhou

Dominik Neumann

Florin Cristian Ghesu

Yefeng Zheng

Young-Ho Kim

Wen Hong Liu

Thomas Pheiffer

Terrence Chen

David D Liu

Li Zhang

Daphne Yu

Kaloian Petkov

Razvan Ioan Ionasec

Daguang Xu

Razvan Ionasec

Nicholas Ayache

Herve Delingette

Christian DeBuys

Shanhui Sun

Vivek Kumar Singh

Klaus Engel

Bin Lou

Charles Henri Florin

Xudong Zheng

Ahmet Tuysuzoglu

Chloe Audigier

Ismayil M Guracar

Boris Mailhe

Marcel Dominik Nickel

Lucian Mihai Itu

Kutay F Ustuner

Michael Suehling

Marie-Pierre Jolly

Estelle Camus

Zhoubing Xu

Noha Youssry El-Zehiry

Jingdan Zhang

Sandra Sudarsky

Peter Mountney

Yang Wang

Bimba Rao

Alexander Brost

Rickard C Loftman

Alexander Michael Gigler

Jiannan Zheng

Yue Zhang

Olivier Ecabert

Bibo Shi

Tanja Kurzendorfer

Philipp Seegerer

Sebastien Piat

Abdoul Aziz Amadou

Jan Boese

Henry R Halperin

Frank Sauer

Atilla Peter Kiraly

Maximilian Fleischer

Berthold Kiefer

Stephan Kannengiesser

Liexiang Fan

Matthias John

Mehmet Akif Gulsun

Stefan Kluckner

Gabriel Haras

Andreas Keller

Philipp Hoelzer

Jin-hyeong Park

Chris Schwemmer

Huseyin Tek

Jerome Declerck

Olivier Pauly

Max Schoebinger

Thomas Boettger

Pascal Ceccaldi

Michael Sühling

Daniel Toth

Francisco Pereira

Dime Vitanovski

Andrzej Milkowski

Ying Chi

Max Schöbinger

James S Duncan

Erin Girard

Systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions.

Smart image navigation for intracardiac echocardiography

For quantification of blood flow by an ultrasound system, B-mode images generated with a multi-transmit, coherent image formation produce swirling or other speckle patterns in the blood regions. These patterns, as represented in specially formed B-mode images, are tracked over time to indicate two or three-dimensional velocity vectors of the blood at a B-mode resolution. Various visualizations may be provided at the same resolution, including the velocity flow field, flow direction, vorticity, vortex size, vortex shape, and/or divergence.

Quantification of blood flow with ultrasound B-mode imaging

For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.

Cardiac flow detection based on morphological modeling in medical diagnostic ultrasound imaging

DCE MR images are obtained from a MR scanner and under a free-breathing protocol is provided. A neural network assigns a perfusion metric to DCE MR images. The neural network includes an input layer configured to receive at least one DCE MR image representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image representative of a second contrast enhancement state and of a second respiratory motion state. The neural network further includes an output layer configured to output at least one perfusion metric based on the at least one DCE MR image and the at least one further DCE MR image. The neural network with interconnections between the input layer and the output layer is trained by a plurality of datasets, each of the datasets having an instance of the at least one DCE MR image and of the at least one further DCE MR image for the input layer and the at least one perfusion metric for the output layer.

Technique for assigning a perfusion metric to DCE MR images

Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Method and system for image registration using an intelligent artificial agent

Systems and method for assisted catheter steering are provided. Instructions for steering a catheter within a patient are received. A graph defining paths between a plurality of configurations of a robotic catheter navigation system is constructed based on the received instructions. Each of the plurality of configurations are associated with a respective view of the patient. A path is determined in the graph to a target configuration of the plurality of configurations of the robotic catheter navigation system. The catheter is automatically steered within the patient based on the determined path in the graph to recover the respective view of the patient associated with the target configuration.

Assisted steering of intracardiac echocardiogram catheters

Systems and methods for estimating local conductivities from anatomical information derived from MR images, ECG, and sparse contact maps are provided. ECG features and sparse measurements are mapped to an anatomical model represented as a graph. Graph convolutional layers and a multilayer perceptron are applied to extract local and global features respectively. The local and global features are combined and further processed by a series of fully connected layers to regress a set of vertex conductivities.

Real-time estimation of local cardiac tissue properties and uncertainties based on imaging and electro-anatomical maps

An ultrasound imager provides for LAA closure guidance. Using ultrasound imaging allows for modeling over time (e.g., throughout a heart cycle). An anatomy model of the LAA over time is used to create a biomechanical model personalized to the patient. The personalized models and a model of one or more closure devices are used to select a closure device for the patient appropriate for the entire heart cycle and to guide placement of the selected closure device during an implantation.

Left atrial appendage closure guidance in medical imaging

A robotic catheter navigation system, and a method for operating the robotic catheter navigation system, are provided. The robotic catheter navigation system comprises a catheter handle, a motor, and a torque transfer disk. The catheter handle comprises a set of gears coupled to a first shaft. The motor is for rotating a second shaft. The torque transfer disk is coupled to the first shaft and the second shaft for transferring the rotation of the second shaft to the first shaft to thereby rotate the set of gears for steering a catheter.

Interlocking gear system for sterile robotic catheter navigation systems

A method, apparatus, and computer readable storage medium are provided herein for constructing a representation of an annular structure associated with an anatomical object. The method includes receiving three-dimensional image data of the anatomical object and detecting at least a first landmark point and a second landmark point on the annular structure. A plane positioned between the first landmark point and the second landmark point, and oriented in accordance with a predefined angular relationship to a line connecting the first landmark point and the second landmark point is determined. A third landmark point on the annular structure which lies in the plane is also detected and the representation of the annular structure is generated using at least the first landmark point, the second landmark point, and the third landmark point. The representation is then outputted.

Annular structure representation

Systems and methods for automatically determining an assessment of a patient are provided. A patient is automatically interacted with, by a first trained machine learning based model, to acquire initial patient data. One or more risk factors associated with the patient are automatically determined, by a second trained machine learning based model, based on the received initial patient data. The patient is automatically interacted with, by the first trained machine learning based model, to acquire additional patient data based on the one or more determined risk factors. An assessment of the patient is automatically determined, by the second trained machine learning based model, based on the initial patient data and the additional patient data. The assessment of the patient is output.

Universal health machine for the automatic assessment of patients

The disclosure relates to techniques for automatically characterizing liver tissue of a patient, comprising receiving morphological magnetic resonance image data set and at least one magnetic resonance parameter map of an imaging region comprising at least partially the liver of the patient, each acquired by a magnetic resonance imaging device, via a first interface. The techniques further include applying a trained function comprising a neural network to input data comprising at least the image data set and the parameter map. At least one tissue score describing the liver tissue is generated as output data, which is provided using a second interface.

Techniques for automatically characterizing liver tissue of a patient

Systems and methods are provided for enhancing a medical image. An initial medical image having an initial field of view is received. An augmented medical image having an expanded field of view is generated using a trained machine learning model. The expanded field of view comprises the initial field of view and an augmentation region. The augmented medical image is output.

Medical image enhancement

Growing community of inventors

Plainsboro, NJ, United States of America

Tommaso Mansi