Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Healthineers Ag

Siemens Healthcare Diagnostics Gmbh

Siemens Medical Solutions USA, Inc.

Institut National De Recherche En Informatique Et En Automatique

Inria

Saikiran Rapaka

Puneet Sharma

Dorin Comaniciu

Ali Kamen

Tiziano Passerini

Lucian Mihai Itu

Tommaso Mansi

Bogdan Georgescu

Viorel Mihalef

Thomas Redel

Chris Schwemmer

Noha Youssry El-Zehiry

Max Schoebinger

Yefeng Zheng

Mehmet Akif Gulsun

Xudong Zheng

Chloe Audigier

Jan Boese

Gareth Funka-Lea

Nicholas Ayache

Pooyan Sahbaee Bagherzadeh

Herve Delingette

Thomas Flohr

Frank Sauer

Atilla Peter Kiraly

Carol L Novak

Liexiang Fan

Stefan Kluckner

Gabriel Haras

Oliver Hayden

Michaela Schmidt

Dominik Neumann

Anton Schick

Hasan Ertan Cetingul

Jens Wetzl

Lukas Richter

Christian Geppert

Francisco Pereira

Olivier Ecabert

Ingo Schmuecking

Rainer Kaergel

Jonathan Sperl

Matthias Ugele

Max Schöbinger

Alma Gregory Sorensen

Teodora Chitiboi

Paul Klein

Uwe Philippi

Justin Hodges

Oliver Zettinig

Francois Forlot

Rainer Kärgel

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

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 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

Systems and methods for predicting a location for acquiring a target view of an anatomical object of interest in an input image are provided. An input image of an anatomical object of interest of a patient is received. An output image is generated using a machine learning based network. The output image depicts a projection of a 3D image plane for acquiring a target view of the anatomical object of interest identified on the input image. The output image is output.

Automatic, dynamic, and adaptive slice planning for cardiac MRI acquisition

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

A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

Identifying the quality of the cell images acquired with digital holographic microscopy using convolutional neural networks

A method and system for fast non-invasive computer-based computation of a hemodynamic index, such as fractional flow reserve (FFR) from medical image data of a patient is disclosed. A patient-specific anatomical model of one or more arteries of a patient is automatically generated based on medical image data of the patient. Regions in the automatically generated patient-specific anatomical model for which user feedback is required for accurate computation of a hemodynamic index are predicted using one or more trained machine learning models.

System and methods for fast computation of computed tomography based fractional flow reserve

Methods for computing hemodynamic quantities include: (a) acquiring angiography data from a patient; (b) calculating a flow and/or calculating a change in pressure in a blood vessel of the patient based on the angiography data; and (c) computing the hemodynamic quantity based on the flow and/or the change in pressure. Systems for computing hemodynamic quantities and computer readable storage media are described.

Computation of hemodynamic quantities from angiographic data

A cell visualization system includes a digital holographic microscopy (DHM) device, a training device, and a virtual staining device. The DHM device produces DHM images of cells and the virtual staining device colorizes the DHM images based on an algorithm generated by the training device using generative adversarial networks and unpaired training data. A computer-implemented method for producing a virtually stained DHM image includes acquiring an image conversion algorithm which was trained using the generative adversarial networks, receiving a DHM image with depictions of one or more cells and virtually staining the DHM image by processing the DHM image using the image conversion algorithm. The virtually stained DHM image includes digital colorization of the one or more cells to imitate the appearance of a corresponding actually stained cell.

Virtual staining of cells in digital holographic microscopy images using general adversarial networks

Systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received. The aorta is segmented from the medical image. One or more measurement planes are identified on the segmented aorta. At least one measurement is calculated at each of the one or more measurement planes. The aorta of the patient is evaluated based on the at least one measurement calculated at each of the one or more measurement planes.

Automatic detection and quantification of the aorta from medical images

Synthetic data-driven hemodynamic determination in medical imaging

A computer-implemented method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a DHM image acquired using a digital holographic microscopy system. The DHM image comprises depictions of one or more cell objects and background. A reference image is generated based on the DHM image. This reference image may then be used to reconstruct a fringe pattern in the DHM image into an optical depth map.

Auto-referencing in digital holographic microscopy reconstruction

A system and method includes acquisition of a first plurality of images, determination, for each of the first plurality of images, of whether an infarct is depicted in the image and generating a label associated with the image based on the determination, acquisition of a second plurality of non-contrast-enhanced computed tomography images, determination, for each of the second plurality of non-contrast-enhanced computed tomography images, of a corresponding one of the first plurality of images, association of each of the second plurality of non-contrast-enhanced computed tomography images with the label associated with its corresponding one of the first plurality of images, and training of a neural network to output a network-generated label, the training based on the second plurality of non-contrast-enhanced computed tomography images and associated labels.

Detection of infarcts using trained network

A method comprises: inputting contrast enhancement data for at least one organ of a patient, at least one patient attribute of the patient, and a test bolus data or bolus tracking data to a regressor; receiving a contrast agent administration protocol from the regressor, based on the contrast enhancement data, the at least one patient attribute and the test bolus or bolus tracking data; and injecting a contrast agent into the patient according to the received contrast agent administration protocol.

Machine-learning based contrast agent administration

Growing community of inventors

Pennington, NJ, United States of America

Saikiran Rapaka