Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Siemens Corporation

Razvan Ioan Ionasec

Dorin Comaniciu

Bogdan Georgescu

Ingmar Voigt

Yefeng Zheng

Dime Vitanovski

Puneet Sharma

Jan Boese

Sasa Grbic

Tommaso Mansi

Yang Wang

Viorel Mihalef

Fernando Vega-Higuera

Helene C Houle

Shaohua Kevin Zhou

Matthias John

Estelle Camus

Dominik Bernhardt

Peter Mountney

Martin Huber

Alexey Tsymbal

Etienne Assoumou Mengue

Andreas Greiser

Lucian Mihai Itu

Michael Scheuering

Edgar Mueller

Gareth Funka-Lea

Klaus Klingenbeck

Fabian Moerchen

Andreas Maier

Sebastian Vogt

Dmitriy Fradkin

Saurabh Datta

Andrey Torzhkov

Gayle M Wittenberg

Kristof Ralovich

Dominik Zaeuner

Waldemar Krawtschuk

Markus Kaiser

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

A method and system for fusion of multi-modal volumetric images is disclosed. A first image acquired using a first imaging modality is received. A second image acquired using a second imaging modality is received. A model and of a target anatomical structure and a transformation are jointly estimated from the first and second images. The model represents a model of the target anatomical structure in the first image and the transformation projects a model of the target anatomical structure in the second image to the model in the first image. The first and second images can be fused based on estimated transformation.

Method and system for model-based fusion of multi-modal volumetric images

A method and system for automatic aortic valve calcification evaluation is disclosed. A patient-specific aortic valve model in a 3D medical image volume, such as a 3D computed tomography (CT) volume. Calcifications in a region of the 3D medical image volume defined based on the aortic valve model. A 2D calcification plot is generated that shows locations of the segmented calcifications relative to aortic valve leaflets of the patient-specific aortic valve model. The 2D calcification plot can be used for assessing the suitability of a patient for a Transcatheter Aortic Valve Replacement (TAVI) procedure, as well as risk assessment, positioning of an aortic valve implant, and selection of a type of aortic valve implant.

Method and system for aortic valve calcification evaluation

A method and system for registering ultrasound images and physiological models to x-ray fluoroscopy images is disclosed. A fluoroscopic image and an ultrasound image, such as a Transesophageal Echocardiography (TEE) image, are received. A 2D location of an ultrasound probe is detected in the fluoroscopic image. A 3D pose of the ultrasound probe is estimated based on the detected 2D location of the ultrasound probe in the fluoroscopic image. The ultrasound image is mapped to a 3D coordinate system of a fluoroscopic image acquisition device used to acquire the fluoroscopic image based on the estimated 3D pose of the ultrasound probe. The ultrasound image can then be projected into the fluoroscopic image using a projection matrix associated with the fluoroscopic image. A patient specific physiological model can be detected in the ultrasound image and projected into the fluoroscopic image.

Method and system for registration of ultrasound and physiological models to X-ray fluoroscopic images

A method and system for physiological image registration and fusion is disclosed. A physiological model of a target anatomical structure in estimated each of a first image and a second image. The physiological model is estimated using database-guided discriminative machine learning-based estimation. A fused image is then generated by registering the first and second images based on correspondences between the physiological model estimated in each of the first and second images.

Method and system for physiological image registration and fusion

A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT) or echocardiography image data, of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A patient specific anatomical heart model is generated by integrating the individual models for each of the heart components.

Method and system for generating a personalized anatomical heart model

Methods, systems, and apparatus for obtaining a sequence of x-ray images are disclosed. An object of interest in a first x-ray image is detected and an area of interest, based on a predicted motion of the object of interest, is determined. A second x-ray image of the area of interest is acquired using spatial x-ray modification to control an x-ray to pass through a portion of a patient corresponding to the area of interest.

Method and system for obtaining a sequence of x-ray images using a reduced dose of ionizing radiation

Physically-constrained modeling of a heart is provided. Patient-specific data may be used to estimate heart anatomy locations. A model is applied to the data for estimation. For increased accuracy of estimation, the biomechanics of the heart, such as the valve, may be used to constrain the estimation. By applying a dynamic system between estimated anatomy locations of different times, the locations may be deformed or refined. The modeled heart and/or valve may be used to estimate hemodynamics. The resulting velocities or other motion information may be used to emulate ultrasound Doppler imaging for comparing with acquired ultrasound Doppler data. The comparison may validate the modeling.

Physically-constrained modeling of a heart in medical imaging

A system and method for regression-based segmentation of the mitral valve in 2D+t cardiac magnetic resonance (CMR) slices is disclosed. The 2D+t CMR slices are acquired according to a mitral valve-specific acquisition protocol introduced herein. A set of mitral valve landmarks is detected in each 2D CMR slice and mitral valve contours are estimated in each 2D CMR slice based on the detected landmarks. A full mitral valve model is reconstructed from the mitral valve contours estimated in the 2D CMR slices using a trained regression model. Each 2D CMR slice may be a cine image acquired over a full cardiac cycle. In this case, the segmentation method reconstructs a patient-specific 4D dynamic mitral valve model from the 2D+t CMR image data.

Method and system for regression-based 4D mitral valve segmentation from 2D+t magnetic resonance imaging slices

Different intracardiac echocardiography volumes are stitched together. Different volumes of a patient are scanned with ICE. To stitch the volumes together, creating a larger volume, the volumes are spatially aligned. The alignment is based on feature, surface, or both feature and surface matching of the ICE volumes with a preoperative model of the same patient. The matching with the model indicates a relative position of the ICE volumes with each other. Using machine-trained classifiers may speed performance, allowing for real-time assembling of a volume from ICE data as the catheter is moved within the patient.

Model-based positioning for intracardiac echocardiography volume stitching

A method and system for non-invasive hemodynamic assessment of aortic coarctation from medical image data, such as magnetic resonance imaging (MRI) data is disclosed. Patient-specific lumen anatomy of the aorta and supra-aortic arteries is estimated from medical image data of a patient, such as contrast enhanced MRI. Patient-specific aortic blood flow rates are estimated from the medical image data of the patient, such as velocity encoded phase-contrasted MRI cine images. Patient-specific inlet and outlet boundary conditions for a computational model of aortic blood flow are calculated based on the patient-specific lumen anatomy, the patient-specific aortic blood flow rates, and non-invasive clinical measurements of the patient. Aortic blood flow and pressure are computed over the patient-specific lumen anatomy using the computational model of aortic blood flow and the patient-specific inlet and outlet boundary conditions.

Method and system for hemodynamic assessment of aortic coarctation from medical image data

A mitral valve is detected in transthoracic echocardiography. The ultrasound transducer is positioned against the chest of the patient rather than being inserted within the patient. While data acquired from such scanning may be noisier or have less resolution, the mitral valve may still be automatically detected. Using both B-mode data representing tissue as well as flow data representing the regurgitant jet, the mitral valve may be detected automatically with a machine-learnt classifier. A series of classifiers may be used, such as determining a position and orientation of a valve region with one classifier, determining a regurgitant orifice with another classifier, and locating mitral valve anatomy with a third classifier. One or more features for some of the classifiers may be calculated based on the orientation of the valve region.

Mitral valve detection for transthoracic echocardiography

A method and system for assessment of virtual stent implantation in an aortic aneurysm is disclosed. A patient-specific 4D anatomical model of the aorta is generated from the 4D medical imaging data. A model representing mechanical properties of the aorta wall is adjusted to reflect changes due to aneurysm growth at a plurality of time stages. A stable deformation configuration of the aorta is generated for each time stages by performing fluid structure interaction (FSI) simulations using the patient-specific 4D anatomical model at each time stage based on the adjusted model representing the mechanical properties of the aorta wall at each time stage. Virtual stent implantation is performed for each stable deformation configuration of the aorta and FSI simulations are performed for each virtual stent implantation.

Method and system for patient-specific hemodynamic assessment of virtual stent implantation

A method and system for automated intervention planning for transcatheter aortic valve implantations using computed tomography (CT) data is disclosed. A patient-specific aortic valve model is detected in a CT volume of a patient. The patient-specific aortic valve model is detected by detecting a global location of the patient-specific aortic valve model in the CT volume, detecting aortic valve landmarks based on the detected global location, and fitting an aortic root surface model. Angulation parameters of a C-arm imaging device for acquiring intra-operative fluoroscopic images and anatomical measurements of the aortic valve are automatically determined based on the patient-specific aortic valve model.

Method and system for intervention planning for transcatheter aortic valve implantation from 3D computed tomography data

Valve treatment simulation is performed from patient specific imaging data for therapy planning. A model of the valve may be generated from the patient specific data automatically or with very minimal user indication of anatomy locations relative to an image. Any characteristics for the valve not extracted from images of the patient may be added to create a volumetric model. Added characteristics include chordae, such as chordae length and leaflet fiber direction. The characteristics may be adjusted based on user feedback and/or comparison with images of the patient. The effect of therapy on closure of the valve may be simulated from the model. For instance, mitral clip intervention is simulated on the patient-specific model. Valves are deformed according to the clip location. Valve closure is then simulated to predict effect of the therapy in terms of mitral regurgitation.

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Lawrenceville, NJ, United States of America

Razvan Ioan Ionasec