Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Yale University

Siemens Healthcare Diagnostics Gmbh

Inria

Puneet Sharma

Dorin Comaniciu

Lucian Mihai Itu

Tiziano Passerini

Saikiran Rapaka

Bogdan Georgescu

Ali Kamen

Viorel Mihalef

Mehmet Akif Gulsun

Yefeng Zheng

Thomas Redel

Frank Sauer

Tommaso Mansi

Vivek Kumar Singh

Chris Schwemmer

Max Schoebinger

Xudong Zheng

Shaohua Kevin Zhou

Razvan Ioan Ionasec

Hien Nguyen

Ingo Schmuecking

Xiaoguang Lu

Dominik Neumann

Ulrich Hartung

Max Schöbinger

Teodora Chitiboi

Terrence Chen

Atilla Peter Kiraly

Michael Scheuering

Pooyan Sahbaee Bagherzadeh

Athira Jane Jacob

Paul Klein

Diana Ioana Stoian

Costin Florian Ciusdel

Thomas Flohr

Jan Boese

Mariappan S Nadar

Shanhui Sun

Sasa Grbic

Benjamin L Odry

Juan Xu

Zhoubing Xu

Ingmar Voigt

Daguang Xu

Fernando Vega-Higuera

Dominik Bernhardt

Jin-hyeong Park

Huseyin Tek

Mingqing Chen

Dime Vitanovski

Serkan Cimen

Tae Soo Kim

Chloe Audigier

Hui Ding

Teodora Marina Chitiboi

Ruth J Soenius

Andrei Bogdan Gheorghita

Boris Mailhe

Carol L Novak

David D Liu

Shun P Miao

Daphne Yu

Stefan Kluckner

Gareth Funka-Lea

Xiao Chen

Razvan Ionasec

Qiu Wang

Jingdan Zhang

Michaela Schmidt

Vibhas S Deshpande

Hasan Ertan Cetingul

Nicholas Ayache

Yang Wang

Fabian Moerchen

Alexander Brost

Jens Wetzl

Feng Qiu

Martin Kramer

Dingxin Wang

Christian Geppert

Martin Berger

Dmitriy Fradkin

Wei Hong

Ahmet Tuysuzoglu

Rainer Kaergel

Peter Kollasch

Jonathan Sperl

Herve Delingette

Anamaria Vizitiu

James S Duncan

Alexandru Turcea

Andrey Torzhkov

Athira Jacob

Poikavila Ullaskrishnan

Indraneel Borgohain

Kristof Ralovich

Gayle M Wittenberg

Guillaume Roffé

Constantin Suciu

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

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

Systems and methods for performing a medical imaging analysis task are provided. An input medical image in a first modality is received. Features are extracted from the input medical image using a first machine learning based encoding network. A medical imaging analysis task is performed on the input medical image based on the extracted features by, in one embodiment, decoding the extracted features to generate results of the medical imaging analysis task using a machine learning based decoding network. Results of the medical imaging analysis task are output. In one embodiment, the first machine learning based encoding network is jointly trained with a second machine learning based encoding network with an unsupervised loss using unannotated pairs of training images. Each of the unannotated pairs comprise a first training image in the first modality and a second training image in a second modality. In one embodiment, the first machine learning based encoding network is also jointly trained with the machine learning based decoding network with a supervised loss using annotated training images.

Semi-supervised learning leveraging cross-domain data for medical imaging analysis

Systems and methods for assessing cardiovascular disease of a patient are provided. Patient data of the patient is received. The patient data may include one or more input medical images of a chest of the patient, results of an assessment of a lung disease performed based on the one or more input medical images, demographic and clinical data of the patient, and cardiovascular imaging exams of the patient. One or more risk scores are computed for the patient based on the patient data using a trained machine learning based network.

Cardiovascular assessment of patients suspected of having COVID-19

Systems and methods for graph based assessment of a patient are provided. Medical imaging data and non-imaging medical data of a patient are received. The medical imaging data and the non-imaging medical data are encoded into encoded features using a graph based machine learning network by comparing the patient with patients of a patient population based on a graph of the patient population. An assessment of the patient is determined based on the encoded features using a machine learning classifier network. The assessment of the patient is output.

Subpopulation based patient risk prediction using graph attention networks

Systems and methods for automatic assessment of a lesion are provided. One or more input medical images of a vessel of a patient is received. A lesion is defined in the one or more input medical images. A region of interest around the lesion is defined in the one or more input medical images. Radiomic features are extracted from the region of interest. An assessment of the lesion is determined using a machine learning based classifier network based on the radiomic features. The assessment of the lesion is output.

Fully automated assessment of coronary vulnerable plaque in coronary CT images using radiomic features

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

Techniques of training an image-synthesis ML algorithm are disclosed. The image-synthesis ML algorithm can be used to generate synthetic imaging data. The synthetic imaging data can be used, in turn, to train a further ML algorithm. The further ML algorithm may be configured to perform image-processing tasks on the respective imaging data.

On-site training of a machine-learning algorithm for generating synthetic imaging data

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

A technique for determining a cardiac metric from rest and stress perfusion cardiac magnetic resonance (CMR) images is provided. A neural network system for determining at least one cardiac metric from CMR images comprises an input layer configured to receive at least one CMR image representative of a rest perfusion state and at least one CMR image representative of a stress perfusion state. The neural network system further comprises an output layer configured to output at least one cardiac metric based on the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state. The neural network system with interconnections between the input layer and the output layer is trained by a plurality of datasets. Each of the datasets comprises an instance of the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state for the input layer and the at least one cardiac metric for the output layer.

Technique for determining a cardiac metric from CMR images

Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.

Multi-view matching across coronary angiogram images

Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks comprises segmentation of reference walls of the vessel from the one or more input medical images and segmentation of lumen of the vessel from the one or more input medical images. Results of the plurality of vessel assessment tasks are output.

Coronary lumen and reference wall segmentation for automatic assessment of coronary artery disease

Systems and methods for determining a quantity of interest of a patient comprise receiving patient data of the patient at a first physiological state. A value of a quantity of interest of the patient at the first physiological state is determined based on the patient data. The quantity of interest represents a medical characteristic of the patient. Features are extracted from the patient data, wherein the features which are extracted are based on the quantity of interest to be determined for the patient at a second physiological state. The value of the quantity of interest of the patient at the first physiological state is mapped to a value of the quantity of interest of the patient at the second physiological state based on the extracted features.

System and method for machine learning based mapping function of patient data from one physiological state to another based on computational fluid dynamics simulation of blood via offline training

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Monmouth Junction, NJ, United States of America

Puneet Sharma