Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Tiziano Passerini

Puneet Sharma

Lucian Mihai Itu

Dorin Comaniciu

Tommaso Mansi

Saikiran Rapaka

Bogdan Georgescu

Thomas Redel

Ali Kamen

Mehmet Akif Gulsun

Chris Schwemmer

Viorel Mihalef

Max Schoebinger

Ingo Schmuecking

Paul Klein

Marie-Pierre Jolly

Shun P Miao

Dominik Neumann

Alexander Brost

Ahmet Tuysuzoglu

Martin Berger

Philipp Seegerer

Costin Florian Ciusdel

Terrence Chen

Thomas Flohr

Yefeng Zheng

Vivek Kumar Singh

Ankur Kapoor

Gabriel Haras

Helene C Houle

Huseyin Tek

Olivier Pauly

Olivier Ecabert

Roch Mollero

Serkan Cimen

Bibo Shi

Ulrich Hartung

Max Schöbinger

Felix Meister

Alexandru Turcea

Athira Jacob

Diana Ioana Stoian

Indraneel Borgohain

Huanhuan Yang

Rapaka Saikiran

Desiree Komuves

Chloé Audigier

Martin Von Roden

Luis Carlos Garcia-Peraza Herrera

Edmond Astolfi

Eric Lluch Alvarez

Guillaume Roffé

A system includes propagation logic configured to obtain one or more contours for one or more directed viewframes within viewframe data. The one or more contours each having a set of tracking points. The viewframe data further includes intermediate viewframes among the one or more directed viewframes. The propagation logic is configured propagate the one or more contours across the intermediate viewframes via iterative viewframe-to-viewframe propagation. The iterative viewframe-to-viewframe propagation include optical flow analysis to determine candidate locations for tracking points followed by one or more validations using motion priors and/or resolved feature tracking.

Contour motion propagation across viewframe data

Angiography angles are determined. Patient information and target vessel information are obtained, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged. At least one angiography angle is determined based on the patient information and the target vessel information. Angiograms obtained using the at least one angiography angle are analyzed to determine a vessel coverage of the target vessel and based on the vessel coverage determines additional angiography angles.

Method and system for improved vessel coverage from angiography

For shape determination of cardiac anatomy with a medical imager, irregularities in motion, poor image quality, and misalignment of imaging planes are counteracted by a process relying on alignment of contours in combination with selection and fitting of a motion model. Contours are extracted from 2D images and aligned for each frame, which is extracted from the sequence of 2D images. The alignment may use a translation for each frame and rotation across frames for improved performance. A motion model is fit to the aligned contours and tested. If insufficient (greater than threshold difference), other motion models are aligned and tested. Motion models may be created on demand for improved performance. If sufficient, the shape of the heart structure is determined from the fit model.

Robust shape determination for cardiac anatomy in medical imaging

Heart strain determination includes receiving a series of 2D-slice images as input. A pose estimation module estimates a slicing-pose of the inputted series of 2D-slice images in the heart. A 3D deformation estimation module estimates a 3D deformation field from the series of 2D-slice images and the estimated slicing-pose. A strain measurement module computes a heart strain measure from the 3D deformation field and a predefined definition for strain computation.

System and method for heart strain determination

Systems and methods for determining a semantic image understanding of medical imaging studies are provided. A plurality of medical imaging studies associated with a plurality of medical imaging modalities is provided. Metadata associated with each of the plurality of medical imaging studies is generated by performing a plurality of semantic image analysis tasks using one or more machine learning based networks. The metadata associated with each of the plurality of medical imaging studies is output.

Cardio AI smart assistant for semantic image analysis of medical imaging studies

In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

Data-driven plaque determination in medical imaging

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

A method includes processing at least one input dataset (using a multi-level processing algorithm, one or more of the at least one input dataset comprising imaging data of an echocardiography of a cardiovascular system of a patient. The multi-level processing algorithm comprises a multi-task level and a consolidation-task level. An input of the consolidation-task level is coupled to an output of the multi-task level. The multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset. The consolidation-task level is configured to determine a fitness of the cardiovascular system of the patient.

Cardiac function assessment using machine-learning algorithms

One or more example embodiments of the present invention relates to a method for the automated determination of examination results in an image sequence from multiple chronologically consecutive frames, the method comprising determining diagnostic candidates in the form of contiguous image regions in the individual frames for a predefined diagnostic finding; and for a number of the diagnostic candidates, determining which candidate image regions in other frames correspond to the particular diagnostic candidate, determining whether the candidate image regions of the particular diagnostic candidate in the other frames overlap with other diagnostic candidates, generating a graph containing the determined diagnostic candidates of the frames as nodes and the determined overlaps as edges, and generating communities from nodes connected via edges.

Method and system for the automated determination of examination results in an image sequence

Systems and methods are provided for training an artificial intelligence model for detecting calcified portions of a vessel in an input medical image. One or more first medical images of a vessel in a first modality and one or more second medical image of the vessel in a second modality are received. Calcified portions of the vessel are detected in the one or more first medical images, The artificial intelligence model is trained for detecting calcified portions of the vessel in the input medical image in the second modality based on the one or more second medical images and the detected calcified portions of the vessel detected in the one or more first medical images.

Assessment of coronary artery calcification in angiographic images

Machine-based risk prediction or assistance is provided for peri-procedural complication, such as peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in PCI. Various combinations of types or modes of information are used in the prediction, such as both imaging and non-imaging data. The prediction may be made prior to, during, and/or after PCI using the machine-learned model to more quickly reduce the chance of PMI. The workflows for prior, during, and/or post PCI incorporate the risk prediction and/or risk-based recommendations to reduce PMI for patients.

Machine-based risk prediction for peri-procedural myocardial infarction or complication from medical data

A method and system for deep learning based cardiac electrophysiological model personalization is disclosed. Electrophysiological measurements of a patient, such as an ECG trace, are received. A computational cardiac electrophysiology model is personalized by calculating patient-specific values for a parameter of the computational cardiac electrophysiology model based at least on the electrophysiological measurements of the patient using a trained deep neural network (DNN). The parameter of the computational cardiac electrophysiology model corresponds to a spatially varying electrical cardiac tissue property.

System and method for deep learning based cardiac electrophysiology model personalization

A computer-implemented method for executing patient management workflows includes acquiring a pre-test dataset of clinically relevant information related to a patient and using a first intelligent agent to identify a diagnostic test for the patient based on the pre-test dataset. Following performance of the diagnostic test, a second intelligent agent is used to select a processing technique to be applied to data collected from the diagnostic test to obtain a diagnostic marker. Following application of the processing technique to the data collected from the diagnostic test, a third intelligent agent is used to generate an optimal patient management plan based on the pre-test dataset, the data collected from the diagnostic test, and the diagnostic marker.

Intelligent agents for patient management

A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest. Post-treatment values for the hemodynamic quantity of interest are predicted at the plurality of points along the coronary artery tree including the multiple points within the lesions for each of one or more candidate treatment options for the patient, resulting in a respective predicted post-treatment pullback curve for the hemodynamic quantity of interest for each of the one or more candidate treatment options. A visualization of a treatment prediction for at least one of the candidate treatment options is displayed.

Non-invasive assessment and therapy guidance for coronary artery disease in diffuse and tandem lesions

Systems and methods for generating an ablation map identifying target ablation locations on a heart of a patient are provided. One or more input medical images of a heart of a patient and a voltage map of the heart of the patient are received. An ablation map identifying target ablation locations on the heart is generated using one or more trained machine learning based models based on the one or more input medical images and the voltage map. The ablation map is output.

Prediction of target ablation locations for treating cardiac arrhythmias using deep learning

Growing community of inventors

Plainsboro, NJ, United States of America

Tiziano Passerini