Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Siemens Healthineers Ag

Institut National De Recherche En Informatique Et En Automatique

The Johns Hopkins University

Julian Krebs

Tommaso Mansi

Sasa Grbic

Shun P Miao

Daguang Xu

Dorin Comaniciu

Bogdan Georgescu

Ali Kamen

Vivek Kumar Singh

Li Zhang

Rui Liao

Florin Cristian Ghesu

Pierre De Tournemire

Ingmar Voigt

Bin Lou

Nicholas Ayache

Herve Delingette

Shaohua Kevin Zhou

Shanhui Sun

Henry R Halperin

Yefeng Zheng

David D Liu

Zhoubing Xu

Razvan Ionasec

Helene C Houle

Dominik Neumann

Chris Schwemmer

Max Schoebinger

Sebastien Piat

Ying Chi

Max Schöbinger

Nora Hünemohr

Pavlo Dyban

Hiroshi Ashikaga

Katherine Chih-ching Wu

Joel Mancina

Systems and methods for medical image registration are provided. A first input medical image and a second input medical image of one or more anatomical objects are received. For each respective anatomical object of the one or more anatomical objects, a region of interest comprising the respective anatomical object is detected in one of the first input medical image or the second input medical image, the region of interest is extracted from the first input medical image and from the second input medical image, and a motion distribution of the respective anatomical object is determined from one of the region of interest extracted from the first input medical image or the region of interest extracted from the second input medical image using a motion model specific to the respective anatomical object. The first input medical image and the second input medical image are registered based on the motion distribution of each respective anatomical object of the one or more anatomical objects to generate a fused image.

Compensation of organ deformation for medical image registration

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 methods for predicting a patient specific risk of cardiac events for cardiac arrhythmia are provided. A medical image sequence of a heart of a patient is received. Cardiac function features are extracted from the medical image sequence. Additional features are extracted from patient data of the patient. A patient specific risk of a cardiac event is predicted based on the extracted cardiac function features and the extracted additional features.

Patient specific risk prediction of cardiac events from image-derived cardiac function features

CT and PET are registered, providing a spatial alignment to be used in attenuation correction for PET reconstruction. A model for machine learning is defined to generate a deformation field. The model is trained with loss based, in part, on the attenuation corrected PET data rather than or in addition to loss based on the uncorrected PET or the generated deformation field. Due to the nature of the mapping from CT to attenuation, a separate, pre-trained network is used to form the attenuation corrected PET data in training the model.

PET-CT registration for medical imaging

For registration of medical images with deep learning, a neural network is designed to include a diffeomorphic layer in the architecture. The network may be trained using supervised or unsupervised approaches. By enforcing the diffeomorphic characteristic in the architecture of the network, the training of the network and application of the learned network may provide for more regularized and realistic registration.

Medical imaging diffeomorphic registration based on machine learning

Systems and methods for performing a medical imaging analysis task using a machine learning based motion model are provided. One or more medical images of an anatomical structure are received. One or more feature vectors are determined. The one or more feature vectors are mapped to one or more motion vectors using the machine learning based motion model. One or more deformation fields representing motion of the anatomical structure are determined based on the one or more motion vectors and at least one of the one or more medical images. A medical imaging analysis task is performed using the one or more deformation fields.

Probabilistic motion model for generating medical images or medical image sequences

Systems and methods for personalized sudden cardiac death risk prediction that generates fingerprints of imaging features of cardiac structure and function. One or more fingerprints and clinical data may be used to generate a risk score. The output risk score may be used to predict the time of death in order to select high-risk patients for implantable cardioverter-defibrillator treatment.

Risk prediction for sudden cardiac death from image derived cardiac motion and structure features

A method and system for computer-based motion estimation and modeling in a medical image sequence of a patient is disclosed. A medical image sequence of a patient is received. A plurality of frames of the medical image sequence are input to a trained deep neural network. Diffeomorphic deformation fields representing estimated motion between the frames of the medical image sequence input to the trained deep neural network are generated. Future motion, or motion between frames, is predicted from the medical image sequence and at least one predicted next frame is generated using the trained deep neural network. An encoding of the observed motion in the medical image sequence is also generated, which is used for motion classification (e.g., normal or abnormal) or motion synthesis to generate synthetic data.

Method and system for deep motion model learning in medical images

Embodiments can provide a method for atlas-based contouring, comprising constructing a relevant atlas database; selecting one or more optimal atlases from the relevant atlas database; propagating one or more atlases; fusing the one or more atlases; and assessing the quality of one or more propagated contours.

Atlas-based contouring of organs at risk for radiation therapy

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

In valve modeling from medical scan data, chordae are modeled as a dense structure. Rather than attempting to provide the same number of chordae (e.g., 25) as found in a human valve, hundreds or thousands of chordae connectors are used. Since solving for lengths of so many chordae may be computationally intensive, the lengths of only a few are solved, and the lengths of the rest of the chordae are derived from the lengths of the few.

Valve modeling with dense chordae from medical scan data

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Moers, Germany

Julian Krebs