Heartflow, Inc.

Sethuraman Sankaran

Leo John Grady

Charles Anthony Taylor, Jr

Christopher K Zarins

Michiel Schaap

Nan Xiao

Gilwoo Choi

Hyun Jin Kim

David Spain

Sophie Khem

Jin Kim

Travis Michael Sanders

Souma Sengupta

David Eberle

Ying Bai

Peter Kersten Petersen

David Lesage

Edward Karl Hahn, Iii

Rhea Tombropoulos

Heather Brown

Amy Tran

Greg Hart

Sophia Khem

Charles A Taylor

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

Systems and methods for treatment planning based on plaque progression and regression curves

Systems and methods are disclosed for simulation of occluded arteries and/or optimization of occlusion-based treatments. One method includes obtaining a patient-specific anatomic model of a patient's vasculature; obtaining an initial computational model of blood flow through the patient's vasculature based on the patient-specific anatomic model; obtaining a post-treatment computational model by modifying portions of the initial computational model based on an occlusion-based treatment; generating a pre-treatment blood flow characteristic using the initial computational model or computing a post-treatment blood flow using the post-treatment computational model; and outputting a representation of the pre-treatment blood flow characteristic or the post-treatment blood flow characteristic.

Systems and methods for simulation of occluded arteries and optimization of occlusion-based treatments

Systems and methods of processing images to determine patient-specific plaque progression based on the processed images

Systems and methods are disclosed for to determining a blood supply and blood demand. One method includes receiving a patient-specific model of vessel geometry of at least a portion of a coronary artery, wherein the model is based on patient-specific image data of at least a portion of a patient's heart having myocardium; determining a coronary blood supply based on the patient-specific model; determining at least a portion of the myocardium corresponding to the coronary artery; determining a myocardial blood demand based on either a mass or a volume of the portion of the myocardium, or based on perfusion imaging of the portion of the myocardium; and determining a relationship between the coronary blood supply and the myocardial blood demand.

Systems and methods for processing electronic images to assess end-organ demand

Systems and methods are disclosed for determining blood flow characteristics of a patient. One method includes: receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order model from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; creating a feature vector comprising the estimates of impedance values and geometric features for each of the one or more points of the patient-specific reduced order model; and determining blood flow characteristics at the one or more points of the patient-specific reduced order model using a machine learning algorithm trained to predict blood flow characteristics based on the created feature vectors at the one or more points.

Systems and methods for estimation of blood flow characteristics using reduced order model and/or machine learning

Systems and methods are disclosed for blood flow simulation. For example, a method may include performing a plurality of blood flow simulations using a first model of vascular blood flow, each of the plurality of blood flow simulations simulating blood flow in a vasculature of a patient or a geometry based on the vasculature of the patient; based on results of the plurality of blood flow simulations, generating a response surface mapping one or more first parameters of the first model to one or more second parameters of a reduced order model of vascular blood; determining values for the one or more parameters of the reduced order model mapped, by the response surface, from parameter values representing a modified state of the vasculature; and performing simulation using the reduced order model parameterized by the determined values, to determine a blood flow characteristic of the modified state of the vasculature.

Systems and methods for estimation of blood flow using response surface and reduced order modeling

Systems and methods are disclosed for assessing a risk of disease. One method includes obtaining an anatomic model associated with a target anatomy; modeling, using a processor, an injection of one or more virtual contrast agents into the anatomic model; performing a simulation of flow of blood and the one or more virtual contrast agents through the anatomic model; and computing one or more characteristics of concentration associated with the one or more virtual contrast agents at one or more locations in the anatomic model based on the simulation.

Systems and methods for virtual contrast agent simulation and computational fluid dynamics (CFD) to compute functional significance of stenoses

Systems and methods are disclosed for assessing the severity of plaque and/or stenotic lesions using contrast distribution predictions and measurements. One method includes: receiving patient-specific images of a patient's vasculature and a measured distribution of a contrast agent delivered through the patient's vasculature; associating the measured distribution of the contrast agent with a patient-specific anatomic model of the patient's vasculature; defining physiological and boundary conditions of a blood flow model of the patient's blood flow and pressure; simulating the distribution of the contrast agent through the patient-specific anatomic model; comparing the measured distribution of the contrast agent and the simulated distribution of the contrast agent through the patient-specific anatomic model to determine whether a similarity condition is satisfied; and updating the defined physiological and boundary conditions and re-simulating distribution of the contrast agent through the one or more points of the patient-specific anatomic model until the similarity condition is satisfied.

Systems and methods for assessing the severity of plaque and/or stenotic lesions using contrast distribution predictions and measurements

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

Systems and methods for risk assessment and treatment planning of arterio-venous malformation

Systems and methods are disclosed for predicting healthy lumen radius and calculating a vessel lumen narrowing score. One method of identifying a lumen diameter of a patient's vasculature includes: receiving a data set including one or more lumen segmentations of known healthy vessel segments of a plurality of individuals; extracting one or more lumen features for each of the vessel segments; receiving a lumen segmentation of a patient's vasculature; determining a section of the patient's vasculature; and determining a healthy lumen diameter of the section of the patient's vasculature using the extracted one or more features for each of the known healthy vessel segments of the plurality of individuals.

Systems and methods for estimating healthy lumen diameter and stenosis quantification in coronary arteries

Embodiments include methods and systems for determining a sensitivity of a patient's blood flow characteristic to anatomical or geometrical uncertainty. For each of one or more of individuals, a sensitivity of a blood flow characteristic may be obtained for one or more uncertain parameters. An algorithm may be trained based on the sensitivities of the blood flow characteristic and one or more of the uncertain parameters for each of the plurality of individuals. A geometric model, a blood flow characteristic, and one or more of the uncertain parameters of at least part of the patient's vascular system may be obtained for a patient. The sensitivity of the patient's blood flow characteristic to one or more of the uncertain parameters may be calculated by executing the algorithm on the blood flow characteristic of at least part of the patient's vascular system, and one or more of the uncertain parameters.

Growing community of inventors

Palo Alto, CA, United States of America

Sethuraman Sankaran