US 12,336,763 B2
Systems and methods for treatment planning based on plaque progression and regression curves
Sethuraman Sankaran, Redwood City, CA (US); Charles A. Taylor, Redwood City, CA (US); Gilwoo Choi, Redwood City, CA (US); Michiel Schaap, Redwood City, CA (US); Christopher K. Zarins, Redwood City, CA (US); and Leo Grady, Redwood City, CA (US)
Assigned to Heartflow, Inc., Mountain View, CA (US)
Filed by HeartFlow, Inc., Redwood City, CA (US)
Filed on Nov. 21, 2019, as Appl. No. 16/691,134.
Application 16/691,134 is a continuation of application No. 15/185,618, filed on Jun. 17, 2016, granted, now 10,517,677.
Application 15/185,618 is a continuation of application No. 14/962,348, filed on Dec. 8, 2015, granted, now 9,649,171, issued on May 16, 2017.
Application 14/962,348 is a continuation of application No. 14/621,129, filed on Feb. 12, 2015, granted, now 9,239,905, issued on Jan. 19, 2016.
Application 14/621,129 is a continuation of application No. 14/522,343, filed on Oct. 23, 2014, granted, now 9,195,801, issued on Nov. 24, 2015.
Claims priority of provisional application 62/033,446, filed on Aug. 5, 2014.
Prior Publication US 2020/0085501 A1, Mar. 19, 2020
This patent is subject to a terminal disclaimer.
Int. Cl. A61B 34/10 (2016.01); A61B 5/00 (2006.01); A61B 5/02 (2006.01); A61B 6/00 (2024.01); A61B 6/03 (2006.01); A61B 6/50 (2024.01); G06F 30/27 (2020.01); G16B 45/00 (2019.01); G16H 40/20 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G16Z 99/00 (2019.01); A61B 5/026 (2006.01); G06F 30/00 (2020.01); G06F 30/20 (2020.01); G06T 7/00 (2017.01)
CPC A61B 34/10 (2016.02) [A61B 5/02007 (2013.01); A61B 5/02028 (2013.01); A61B 5/7275 (2013.01); A61B 6/032 (2013.01); A61B 6/504 (2013.01); A61B 6/507 (2013.01); A61B 6/5217 (2013.01); G06F 30/27 (2020.01); G16B 45/00 (2019.02); G16H 40/20 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G16Z 99/00 (2019.02); A61B 5/026 (2013.01); A61B 2034/105 (2016.02); G06F 30/00 (2020.01); G06F 30/20 (2020.01); G06T 7/0012 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/30101 (2013.01)] 14 Claims
 
1. A computer-implemented method of evaluating a patient with vascular disease, the method comprising:
receiving patient-specific image data regarding a geometry of a patient's vasculature;
creating a patient-specific anatomic model representing at least a portion of a location of a disease in the patient's vasculature based on the received patient-specific image data, the disease comprising an area of plaque in the patient's vasculature;
non-invasively determining a composition of the disease using the received patient-specific image data, the composition of the disease comprising the plaque composition, a change in the plaque composition, or a combination thereof;
for each point of a plurality of points in the image data, generating a respective feature vector based on the non-invasively determined composition of the disease;
predicting a progression potential of the disease based on the composition of the disease using a machine-learning model, wherein the machine-learning model is trained based on (i) training feature vectors corresponding to training imaging data and a determined composition of the disease from a plurality of individuals and (ii) data regarding one or more of disease progression, regression, or onset for the plurality of individuals, wherein the machine-learning model is trained to learn associations between the training feature vectors and the disease progression, regression, and onset for the plurality of individuals, and the machine-learning model is configured to use the learned associations to generate a respective patient-specific estimate of a progression potential of a disease for each point of the plurality of points in the imaging data based on input of the respective feature vectors, predicting the progression potential comprising:
determining a type of the plaque in the patient's vasculature based on the composition of the disease, wherein the type of plaque is at least one of a lipid rich core, a collagen fiber, or hardened calcified plaque; and
determining one or more values of a blood flow characteristic within the patient's vasculature based on the determination of the type of the plaque, wherein determining the one or more values of the blood flow characteristic comprises determining a hemodynamic sensitivity of the plaque;
updating the geometry of the created patient-specific anatomic model, by modifying the created patient-specific anatomic model to incorporate the predicted progression potential; and
outputting a hemodynamic sensitivity curve based on the determined hemodynamic sensitivity of the plaque.