Siemens Medical Solutions Usa, Inc.

Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Healthineers Ag

Siemens Corporation

Ingmar Voigt

Tommaso Mansi

Dorin Comaniciu

Bogdan Georgescu

Razvan Ioan Ionasec

Helene C Houle

Sasa Grbic

Jan Boese

Yefeng Zheng

Razvan Ionasec

Yang Wang

Dime Vitanovski

Puneet Sharma

Estelle Camus

Viorel Mihalef

Dominik Neumann

Bimba Rao

Julian Krebs

Charles Henri Florin

Mihai Scutaru

Etienne Assoumou Mengue

Shaohua Kevin Zhou

Ali Kamen

Marcel Dominik Nickel

Frank Sauer

Rui Liao

Michael Suehling

Matthias John

Mehmet Akif Gulsun

Klaus Engel

Noha Youssry El-Zehiry

Fernando Vega-Higuera

Philipp Hoelzer

Chris Schwemmer

Klaus Klingenbeck

Huseyin Tek

Peter Mountney

Max Schoebinger

Martin Huber

Alexey Tsymbal

Olivier Ecabert

Saurabh Datta

Yue Zhang

Max Schöbinger

Abdoul Aziz Amadou

Athira Jane Jacob

Sebastien Piat

Teodora Marina Chitiboi

Anand Vinod Tatpati

Codruta-Xenia Ene

Stephen Henderson

Anand Tatpati

Joel Mancina

Markus Kaiser

Dominik Zaeuner

For training for and performance of LGE analysis, multi-task machine-learning model is trained to output various cardiac tissue characteristics based on input of LGE MR data. The use of segmentation may be avoided or limited, resulting in a greater number of available training data samples, by using radiology clinical reports with LGE information as a source for samples. The multi-task model may be trained to output cardiac tissue characteristics using radiology clinical reports with LGE information with no segmentation or with segmentation for only a subset of the training samples. By training for multiple tasks, the accuracy of prediction for each task benefits from the information for other tasks. The trained model outputs values of characteristics for multiple tasks, such as extent of enhancement, type of enhancement, and localization of enhancement. Other tasks may be included, such as disease classification. Other inputs may be used, such as also including sensor data and/or cardiac motion.

Late gadolinium enhancement analysis for magnetic resonance 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

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 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

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 and system for estimating physiological heart measurements from medical images and clinical data disclosed. A patient-specific anatomical model of the heart is generated from medical image data of the patient. A patient-specific multi-physics computational heart model is generated based on the patient-specific anatomical model by personalizing parameters of a cardiac electrophysiology model, a cardiac biomechanics model, and a cardiac hemodynamics model based on medical image data and clinical measurements of the patient. Cardiac function of the patient is simulated using the patient-specific multi-physics computational heart model. The parameters can be personalized by inverse problem algorithms based on forward model simulations or the parameters can be personalized using a machine-learning based statistical model.

Systems and methods for estimating physiological heart measurements from medical images and clinical data

A method and system for virtual percutaneous valve implantation is disclosed. A patient-specific anatomical model of a heart valve is estimated based on 3D cardiac medical image data and an implant model representing a valve implant is virtually deployed into the patient-specific anatomical model of the heart valve. A library of implant models, each modeling geometrical properties of a corresponding valve implant, is maintained. The implant models maintained in the library are virtually deployed into the patient specific anatomical model of the heart valve to select an implant type and size and deployment location and orientation for percutaneous valve implantation.

Method and system for virtual percutaneous valve implantation

For measurement point determination in imaging with a medical scanner, the user selects a location on the image. Rather than using that location, an 'intended' location corresponding to a local boundary or landmark represented in the image is identified. The medical scanner uses the simple user interface to more exactly determine points for measurement. One or more rays are cast from the user selected location. The actual location is found by examining data along the ray or rays. For 2D imaging, the rays are cast in the plane. For 3D imaging, the ray is cast along a view direction to find the depth. The intensities along the ray or around the ray are used to find the actual location, such as by application of a machine-learnt classifier to the limited region around the ray or by finding intensities along the ray relative to a threshold.

Measurement point determination in medical diagnostic imaging

Anatomy, such as papillary muscle, is automatically detected () and/or detected in real-time. For automatic detection () of small anatomy, machine-learnt classification with spatial () and temporal (e.g., Markov) () constraints is used. For real-time detection, sparse machine-learnt detection () interleaved with optical flow tracking () is used.

Landmark detection with spatial and temporal constraints in medical imaging

A regurgitant orifice of a valve is detected. The valve is detected from ultrasound data. An anatomical model of the valve is fit to the ultrasound data. This anatomical model may be used in various ways to assist in valvular assessment. The model may define anatomical locations about which data is sampled for quantification. The model may assist in detection of the regurgitant orifice using both B-mode and color Doppler flow data with visualization without the jet. Segmentation of a regurgitant jet for the orifice may be constrained by the model. Dynamic information may be determined based on the modeling of the valve over time.

Valve regurgitant detection for echocardiography

An entire volume is scanned. A sub-volume is separately scanned with different settings for beamforming parameters, allowing greater image quality for the sub-volume while providing context from the volume. The anatomy of interest is periodically detected, and the sub-volume shifted in position to cover the anatomy of interest, allowing for relatively continuous volume imaging with enhanced quality imaging of the sub-volume. Interleaving by volume and sub-volume slices may allow for optimization of relative frame rate and image quality. Different combinations between volume and sub-volume data for anatomy detection and display may provide for desired imaging while allowing the regular detection of the anatomy.

Continuously oriented enhanced ultrasound imaging of a sub-volume

A first interface for reading image data of an anatomical region obtained by means of a medical imaging method is provided. A modeling module serves for establishing a volumetric biomechanical structure model of the anatomical region on the basis of the image data. Moreover, provision is made of a tracking module, couplable with a camera, for video-based registration of spatial gestures of a user. Furthermore, a simulation module, based on the biomechanical structure model, serves to assign a registered gesture to a simulated mechanical effect on the anatomical region, simulate a mechanical reaction of the anatomical region to the simulated mechanical effect, and modify the biomechanical structure model in accordance with the simulated mechanical reaction. Moreover, provision is made for a visualization module for the volumetric visualization of the biomechanical structure model.

Device and method for the computer-assisted simulation of surgical interventions

A method and system for transcatheter aortic valve implantation (TAVI) planning is disclosed. An anatomical surface model of the aortic valve is estimated from medical image data of a patient. Calcified lesions within the aortic valve are segmented in the medical image data. A combined volumetric model of the aortic valve and calcified lesions is generated. A 3D printed model of the heart valve and calcified lesions is created using a 3D printer. Different implant device types and sizes can be placed into the 3D printed model of the aortic valve and calcified lesions to select an implant device type and size for the patient for a TAVI procedure. The method can be similarly applied to other heart valves for any type of heart valve intervention planning.

Method and system for advanced transcatheter aortic valve implantation planning

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Erlangen, Germany

Ingmar Voigt