US 12,004,841 B2
Systems and methods for estimation of blood flow characteristics using reduced order model and/or machine learning
Travis Michael Sanders, Plano, TX (US); Sethuraman Sankaran, Palo Alto, CA (US); Leo Grady, Darien, CT (US); David Spain, Portland, OR (US); Nan Xiao, San Jose, CA (US); Jin Kim, Daly City, CA (US); and Charles A. Taylor, Atherton, CA (US)
Assigned to HeartFlow, Inc., Mountain View, CA (US)
Filed by HeartFlow, Inc., Redwood City, CA (US)
Filed on Apr. 18, 2023, as Appl. No. 18/302,291.
Application 18/302,291 is a continuation of application No. 17/169,912, filed on Feb. 8, 2021, granted, now 11,653,833.
Application 17/169,912 is a continuation of application No. 15/709,195, filed on Sep. 19, 2017, granted, now 10,945,606, issued on Mar. 16, 2021.
Claims priority of provisional application 62/396,965, filed on Sep. 20, 2016.
Prior Publication US 2023/0248242 A1, Aug. 10, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 30/40 (2018.01); A61B 5/00 (2006.01); A61B 5/02 (2006.01); G16B 40/20 (2019.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); A61B 5/026 (2006.01); G16B 45/00 (2019.01)
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:
obtaining a reduced-order model that is representative of at least a portion of a patient-specific anatomic model of at least a portion of a patient's vasculature, the reduced-order model including one or more points having an impedance value based on first estimated values of blood flow characteristics at one or more locations of the portion of the patient-specific anatomic model;
updating the reduced-order model by employing a machine-learning algorithm that has been 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 second estimated values for the blood flow characteristic at the one or more locations of the portion of the patient-specific anatomic model.