Heartflow, Inc.

Gilwoo Choi

Charles Anthony Taylor, Jr

Leo John Grady

Michiel Schaap

Christopher K Zarins

Timothy A Fonte

Hyun Jin Kim

Sethuraman Sankaran

Campbell Rogers

Michael Singer

Ying Bai

Bon-Kwon Koo

Jonathan Tang

Nan Xiao

Rhea Tombropoulos

David Spain

David Eberle

Stanley C Hunley

Buzzy Spain

Charles A Taylor

Timothy Fonte

Leo Grady

Thimothy A Fonte

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 using patient-specific anatomical models and physiological parameters to predict viability of a target tissue or vessel to guide diagnosis or treatment of cardiovascular disease. One method includes: receiving a patient-specific vessel model and a patient-specific tissue model of a patient anatomy; receiving one or more patient-specific physiological parameters (e.g. blood flow, anatomical characteristics, etc.) for one or more physiological states; estimating a viability characteristic of the patient-specific tissue or vessel model (e.g., via a trained machine learning algorithm), using the patient-specific physiological parameters; and outputting the viability characteristic to an electronic storage medium or display.

Systems and methods for predicting tissue viability deficits from physiological, anatomical, and patient characteristics

Computer-implemented methods are disclosed for assessing the effect of musculoskeletal activities on disease and/or clinical events, the method comprising: receiving a patient-specific vascular and musculoskeletal model of a patient's anatomy, including at least one vessel of the patient; receiving at least one characteristic of the patient's musculoskeletal activity; generating or updating a computational anatomic vascular and musculoskeletal model of the patient's anatomy based on the received at least one characteristic of musculoskeletal activity; performing at least one of a computational fluid dynamics analysis or a structural mechanics simulation on the computational anatomic vascular and musculoskeletal model; and estimating at least one of the patient's risk of disease or clinical events based on the performed computational fluid dynamics analysis and/or structural mechanics simulation. Systems and computer readable media for executing these methods are also disclosed.

Systems and methods for cardiovascular blood flow and musculoskeletal modeling for predicting device failure or clinical events

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

Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

Systems and methods for processing electronic images to evaluate medical images

Systems and methods are disclosed for predicting coronary plaque vulnerability, using a computer system. One method includes acquiring anatomical image data of at least part of the patient's vascular system; performing, using a processor, one or more image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating an adverse plaque characteristic based on results of the one or more of image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of the anatomical image data; and reporting, using the processor, the calculated adverse plaque characteristic.

Systems and methods for predicting coronary plaque vulnerability from patient specific anatomic image data

Computer-implemented methods are disclosed for estimating values of hemodynamic forces acting on plaque or lesions. One method includes: receiving one or more patient-specific parameters of at least a portion of a patient's vasculature that is prone to plaque progression, rupture, or erosion; constructing a patient-specific geometric model of at least a portion of a patient's vasculature that is prone to plaque progression, rupture, or erosion, using the received one or more patient-specific parameters; estimating, using one or more processors, the values of hemodynamic forces at one or more points on the patient-specific geometric model, using the patient-specific parameters and geometric model by measuring, deriving, or obtaining one or more of a pressure gradient and a radius gradient; and outputting the estimated values of hemodynamic forces to an electronic storage medium. Systems and computer readable media for executing these methods are also disclosed.

Systems and methods for estimating hemodynamic forces acting on plaque and monitoring risk

Systems and methods are disclosed for identifying and modeling unresolved vessels, and the effects thereof, in image-based patient-specific hemodynamic models. One method includes: receiving, in an electronic storage medium, one or more patient-specific anatomical models representing at least a vessel of a patient; determining, using a processor, the values and characteristics of one or more patient-specific morphometric features in the one or more patient-specific anatomical models; modifying the patient-specific anatomical model using the determined patient-specific morphometric features; and outputting, one or more of, a modified patient-specific anatomical model or a patient-specific morphometric feature to an electronic storage medium or display.

Systems and methods for identifying and modeling unresolved vessels in image-based patient-specific hemodynamic models

Embodiments include methods of identifying a personalized cardiovascular device based on patient-specific geometrical information, the method comprising acquiring a geometric model of at least a portion of a patient's vascular system; obtaining one or more geometric quantities of one or more blood vessels of the geometric model of the patient's vascular system; determining the presence or absence of a pathology characteristic at a location in the geometric model of the patient's vascular system; generating an objective function defined by a plurality of device variables and a plurality of hemodynamic and solid mechanics characteristics; and optimizing the objective function using computational fluid dynamics and structural mechanics analysis to identify a plurality of device variables that result in desired hemodynamic and solid mechanics characteristics.

Systems and methods for identifying personalized vascular implants from patient-specific anatomic data

Systems and methods are disclosed for determining a patient risk assessment or treatment plan based on emboli dislodgement and destination. One method includes receiving a patient-specific anatomic model generated from patient-specific imaging of at least a portion of a patient's vasculature; determining or receiving a location of interest in the patient-specific anatomic model of the patient's vasculature; using a computing processor for calculating blood flow through the patient-specific anatomic model to determine blood flow characteristics through at least the portion of the patient's vasculature of the patient-specific anatomic model downstream from the location of interest; and using a computing processor for particle tracking through the simulated blood flow to determine a destination probability of an embolus originating from the location of interest in the patient-specific anatomic model, based on the determined blood flow characteristics.

Systems and methods for embolism prediction using embolus source and destination probabilities

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

Systems and methods for processing electronic images to predict lesions

Systems and methods are disclosed herein for anatomical modeling using information obtained during a medical procedure, whereby an initial anatomical model is generated or obtained, a correspondence is determined between the initial model and additional data and/or measurements from an invasive or noninvasive procedure, and, if a discrepancy is found between the initial model and the additional data, the anatomical model is updated to incorporate the additional data and reduce the discrepancy.

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Mountain View, CA, United States of America

Gilwoo Choi