US 11,653,833 B2
System and methods for estimation of blood flow characteristics using reduced order model and machine learning
Travis Michael Sanders, Plano, TX (US); Sethuraman Sankaran, Palo Alto, CA (US); Leo Grady, Darien, CT (US); David Spain, San Mateo, CA (US); Nan Xiao, San Jose, CA (US); Jin Kim, Daly City, CA (US); and Charles A. Taylor, Atherton, CA (US)
Assigned to HeartFlow, Inc., Redwood City, CA (US)
Filed by HeartFlow, Inc., Redwood City, CA (US)
Filed on Feb. 8, 2021, as Appl. No. 17/169,912.
Application 17/169,912 is a continuation of application No. 15/709,195, filed on Sep. 19, 2017, granted, now 10,945,606.
Claims priority of provisional application 62/396,965, filed on Sep. 20, 2016.
Prior Publication US 2021/0161384 A1, Jun. 3, 2021
Int. Cl. G06K 9/00 (2022.01); A61B 5/00 (2006.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G16H 50/20 (2018.01); G16B 40/20 (2019.01); A61B 5/02 (2006.01); G16B 45/00 (2019.01); A61B 5/026 (2006.01)
CPC A61B 5/0044 (2013.01) [A61B 5/02007 (2013.01); G16B 40/20 (2019.02); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); A61B 5/026 (2013.01); G16B 45/00 (2019.02)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of determining blood flow characteristics of a patient, the method comprising:
receiving, in an electronic storage medium, a patient-specific anatomical model of at least a portion of the patient's vasculature including geometric features at one or more points;
truncating the patient-specific anatomical model at locations of the patient-specific anatomical model for which blood flow characteristics are to be determined;
applying boundary conditions to the truncated patient-specific anatomical model, the boundary conditions associated with hemodynamics at an inflow of blood flow, outflow of blood flow, and a vessel wall of the truncated patient-specific anatomic model;
using the boundary conditions to estimate a first value for the blood flow characteristic at one or more locations of the truncated patient-specific anatomic model;
generating a reduced-order model that is representative of the truncated patient-specific anatomic model, the reduced-order model including one or more points having an impedance value based on the estimated first value of the blood flow characteristic at the one or more locations of the truncated patient-specific anatomic model;
updating the reduced-order model by employing a machine-learning algorithm that is trained, based on errors determined between at least one impedance value of at least one training reduced-order model and at least one corresponding impedance value determined by computational fluid dynamics, to reduce error in one or more impedance values of an input reduced-order model; and
using the updated reduced-order model to determine a second value for the blood flow characteristic at the one or more locations of the truncated patient-specific anatomic model.