IP Library › Granted Patent US 12,329,462
Granted Patent B2
US 12,329,462 · App. 18/304,655 · Granted Jun 17, 2025

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

Inventors: Charles A. Taylor (Atherton, CA); Hyun Jin Kim (San Mateo, CA); Sophie Khem (San Francisco, CA); Sethuraman Sankaran (Palo Alto, CA); David Spain (Portland, OR); Nan Xiao (San Jose, CA)
Assignee: Heartflow, Inc.
A61B34/10A61B5/0044A61B5/02028A61B5/0263A61B6/032A61B6/037A61B6/503A61B6/504A61B6/507A61B6/5217A61B8/065A61B8/12A61B8/5223G16H50/20G16H50/50G16H50/70A61B5/0035A61B5/0042A61B5/02007A61B5/7275A61B8/0883A61B8/0891A61B2034/105A61B2576/023
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Quick Facts
Patent No.
US 12,329,462
App. No.
18/304,655
Granted
Jun 17, 2025
Kind
B2
Abstract

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.

Claims (56)

1. A computer-implemented method of processing patient-specific images, the method comprising:

receiving patient-specific imaging data generated by an imaging device having an imaging threshold, wherein the patient-specific imaging data includes data representing one or more first blood vessels of a patient, and wherein the patient-specific imaging data lacks data representing one or more second blood vessels of the patient because the one or more second blood vessels are smaller than the imaging threshold;

generating a first portion of a patient-specific model of a patient's vasculature, wherein the first portion of the patient-specific model is based on the patient-specific imaging data, and wherein the first portion of the patient-specific model includes the one or more first blood vessels, which have one or more blood vessel outlets;

generating a second portion of the patient-specific model of blood vessels of the patient's vasculature, wherein the second portion of the patient-specific model models the one or more second blood vessels that are lacking in the patient-specific imaging data because they are smaller than the imaging threshold of the imaging device, and wherein the one or more second blood vessels in the second portion of the patient-specific model are downstream of the one or more blood vessel outlets of the first portion of the patient-specific model;

combining the first portion of the patient-specific model with the second portion of the patient-specific model to create a combined patient-specific model;

using the combined patient-specific model, determining a first metric relating a supply of blood provided by the patient's vasculature to an amount of blood demanded by at least a portion of a tissue or organ receiving blood from the patient's vasculature;

determining whether the first metric indicates a mismatch between the supply of blood provided by the patient's vasculature and the amount of blood demanded by the at least the portion of the tissue or organ receiving blood from the patient's vasculature;

using the first metric to determine a second metric, wherein the second metric is a measure of blood flow, pressure, perfusion, or fractional flow reserve; and

displaying the combined patient-specific model, wherein the displayed combined patient-specific model includes a color spectrum indicating variations in values of the second metric along portions of the combined patient-specific model.

2. The method of claim 1 , further comprising determining a disease state of the patient based on determining that the first metric indicates the mismatch.

3. The method of claim 1 , wherein the second portion of the patient-specific model is based at least in part on population-based data.

4. The method of claim 1 , further comprising:

generating a simulation of blood flow through the patient's vasculature.

5. The method of claim 4 , further comprising:

modifying, based determining that the first metric indicates the mismatch, at least one parameter of the simulation of blood flow through the patient's vasculature.

6. The method of claim 1 , further comprising predicting a location of a plaque rupture in the patient's vasculature based on determining that the first metric indicates the mismatch.

7. A system for image processing patient-specific images, the system comprising:

at least one data storage device storing instructions for processing patient-specific images; and

at least one processor configured to execute the instructions to perform operations comprising:

receiving patient-specific imaging data generated by an imaging device having an imaging threshold, wherein the patient-specific imaging data includes data representing one or more first blood vessels of a patient, and wherein the patient-specific imaging data lacks data representing one or more second blood vessels of the patient because the one or more second blood vessels are smaller than the imaging threshold;

generating a first portion of a patient-specific model of a patient's vasculature, wherein the first portion of the patient-specific model is based on the patient-specific imaging data, and wherein the first portion of the patient-specific model includes the one or more first blood vessels, which have one or more blood vessel outlets;

generating a second portion of the patient-specific model of blood vessels of the patient's vasculature, wherein the second portion of the patient-specific model models the one or more second blood vessels that are lacking in the patient-specific imaging data because they are smaller than the imaging threshold of the imaging device, and wherein the one or more second blood vessels in the second portion of the patient-specific model are downstream of the one or more blood vessel outlets of the first portion of the patient-specific model;

combining the first portion of the patient-specific model with the second portion of the patient-specific model to create a combined patient-specific model;

using the combined patient-specific model, determining a first metric relating a supply of blood provided by the patient's vasculature to an amount of blood demanded by at least a portion of a tissue or organ receiving blood from the patient's vasculature;

determining whether the first metric indicates a mismatch between the supply of blood provided by the patient's vasculature and the amount of blood demanded by the at least the portion of the tissue or organ receiving blood from the patient's vasculature;

using the first metric to determine a second metric, wherein the second metric is a measure of blood flow, pressure, perfusion, or fractional flow reserve; and

displaying the combined patient-specific model, wherein the displayed combined patient-specific model includes a color spectrum indicating variations in values of the second metric along portions of the combined patient-specific model.

8. The system of claim 7 , wherein the operations further comprise:

determining a disease state of the patient based on determining that the first metric indicates the mismatch.

9. The system of claim 7 , wherein the second portion of the patient-specific model is based at least in part on population-based data.

10. The system of claim 7 , wherein the operations further comprise:

generating a simulation of blood flow through the patient's vasculature.

11. The system of claim 10 , wherein the operations further comprise:

modifying, based determining that the first metric indicates the mismatch, at least one parameter of the simulation of blood flow through the patient's vasculature.

12. The system of claim 7 , wherein the operations further comprise:

predicting a location of a plaque rupture in the patient's vasculature based on determining that the first metric indicates the mismatch.

13. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for performing a method of processing patient-specific images, the method comprising:

receiving patient-specific imaging data generated by an imaging device having an imaging threshold, wherein the patient-specific imaging data includes data representing one or more first blood vessels of a patient, and wherein the patient-specific imaging data lacks data representing one or more second blood vessels of the patient because the one or more second blood vessels are smaller than the imaging threshold;

generating a first portion of a patient-specific model of a patient's vasculature, wherein the first portion of the patient-specific model is based on the patient-specific imaging data, and wherein the first portion of the patient-specific model includes the one or more first blood vessels, which have one or more blood vessel outlets;

generating a second portion of the patient-specific model of blood vessels of the patient's vasculature, wherein the second portion of the patient-specific model models the one or more second blood vessels that are lacking in the patient-specific imaging data because they are smaller than the imaging threshold of the imaging device, and wherein the one or more second blood vessels in the second portion of the patient-specific model are downstream of the one or more blood vessel outlets of the first portion of the patient-specific model;

combining the first portion of the patient-specific model with the second portion of the patient-specific model to create a combined patient-specific model;

using the combined patient-specific model, determining a first metric relating a supply of blood provided by the patient's vasculature to an amount of blood demanded by at least a portion of a tissue or organ receiving blood from the patient's vasculature;

determining whether the first metric indicates a mismatch between the supply of blood provided by the patient's vasculature and the amount of blood demanded by the at least the portion of the tissue or organ receiving blood from the patient's vasculature;

using the first metric to determine a second metric, wherein the second metric is a measure of blood flow, pressure, perfusion, or fractional flow reserve; and

displaying the combined patient-specific model, wherein the displayed combined patient-specific model includes a color spectrum indicating variations in values of the second metric along portions of the combined patient-specific model.

14. The non-transitory computer readable medium of claim 13 , wherein the second portion of the patient-specific model is based at least in part on population-based data.

15. The non-transitory computer readable medium of claim 13 , wherein the method further comprises:

generating a simulation of blood flow through the patient's vasculature; and

modifying, based determining that the first metric indicates the mismatch, at least one parameter of the simulation of blood flow through the patient's vasculature.

16. The method of claim 1 , wherein the first metric is a ratio of the supply of blood provided by the patient's vasculature to the amount of blood demanded by the at least the portion of the tissue or organ receiving blood from the patient's vasculature the portion of the tissue or the organ.

17. The method of claim 16 , wherein determining whether the first metric indicates a mismatch includes comparing the ratio of the a supply of blood provided by the patient's vasculature to the amount of blood demanded by the at least the portion of the tissue or organ receiving blood from the patient's vasculature the portion of the tissue or the organ with one or more predetermined values derived from a population of prior patients.

18. The method of claim 1 , further comprising displaying an enhanced image based on the combined patient-specific model which includes the one or more second blood vessels of the patient that are smaller than the imaging threshold.

19. The system of claim 7 , wherein the operations further comprise:

displaying an enhanced image based on the combined patient-specific model which includes the one or more second blood vessels of the patient that are smaller than the imaging threshold.

20. The non-transitory computer readable medium of claim 13 , wherein the method further comprises:

displaying an enhanced image based on the combined patient-specific model which includes the one or more second blood vessels of the patient that are smaller than the imaging threshold.

Assignments (3)
RELEASE OF SECURITY INTEREST Recorded Sep 11, 2025
From: HAYFIN SERVICES LLP
To: HEARTFLOW, INC.
Reel/Frame 072876/0775 →
SECURITY INTEREST Recorded Jun 18, 2024
From: HEARTFLOW, INC.
To: HAYFIN SERVICES LLP
Reel/Frame 067775/0966 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 16, 2023
From: TAYLOR, CHARLES A.; KIM, HYUN JIN; KHEM, SOPHIE; SANKARAN, SETHURAMAN; SPAIN, DAVID; XIAO, NAN
To: HEARTFLOW, INC.
Reel/Frame 063653/0276 →
Continuity (5)
Continuation 17197271 · Mar 10, 2021
Continuation 16691266 · Nov 21, 2019
Continuation 15192286 · Jun 24, 2016
Provisional Application 62236707 · Oct 2, 2015
Related Publication 20230277247A1 · Sep 7, 2023
References Cited (42)
US 6908435B1 · Mueller et al. · 2005 [cited by applicant]
US 8157742B2 · Taylor · 2012 [cited by applicant]
US 8311748B2 · Taylor et al. · 2012 [cited by applicant]
US 8315814B2 · Taylor et al. · 2012 [cited by applicant]
US 8386188B2 · Taylor et al. · 2013 [cited by applicant]
US 8594950B2 · Taylor · 2013 [cited by applicant]
US 8734357B2 · Taylor · 2014 [cited by applicant]
US 8903472B2 · Kassab · 2014 [cited by applicant]
US 9087147B1 · Fonte · 2015 [cited by applicant]
US 20120072190A1 · Sharma et al. · 2012 [cited by applicant]
US 20130054214A1 · Taylor · 2013 [cited by applicant]
US 20140029835A1 · Kim et al. · 2014 [cited by applicant]
US 20140114618A1 · Fonte et al. · 2014 [cited by applicant]
US 20150250395A1 · Igarashi et al. · 2015 [cited by applicant]
US 20150254418A1 · Sankaran et al. · 2015 [cited by applicant]
US 20150297161A1 · Grass et al. · 2015 [cited by applicant]
US 20170105694A1 · Grass et al. · 2017 [cited by applicant]
US 20170286628A1 · Shim et al. · 2017 [cited by applicant]
EP 0542565A1 · 1993 [cited by applicant]
EP 2690598A2 · 2014 [cited by applicant]
JP 2002512832A · 2002 [cited by applicant]
JP 2013534154A · 2013 [cited by applicant]
JP 2013233369A · 2013 [cited by applicant]
JP 2014128650A · 2014 [cited by applicant]
WO 2014042899A2 · 2014 [cited by applicant]
WO 2014091339A1 · 2014 [cited by applicant]
WO 2015030998A3 · 2015 [cited by applicant]
Sugimachi, M., & Kawada, T. (2009). Coronary Artery Volume Noninvasively Measured With Multislice Computed Tomography. Circulation Journal, 73(8), 1395-1396. (Year: 2009). [cited by applicant]
Burton, Rebecca AB, et al. “Three-Dimensional Models of Individual Cardiac Histoanatomy: Tools and Challenges.” Annals of the New York Academy of Sciences 1080.1 (2006): 301-319. [cited by applicant]
Choy, J.S. and G.S. Kassab, Scaling of myocardial mass to flow and morphometry of coronary arteries. J Appl Physiol, 2008. 104(5): p. 1281-6. [cited by applicant]
Ehara, S., Matsumoto, K., Shirai, N., Nakanishi, K., Otsuka, K., Iguchi, T., . . . & Yoshiyama, M. (2013). Typical coronary appearance of dilated cardiomyopathy versus left ventricular concentric hypertrophy: coronary v… [cited by applicant]
Ehara, S., Okuyama, T., Shirai, N., Sugioka, K., OE, H., Itoh, T., . . . & Hozumi, T. (2009). Inadequate Increase in the Volume of Major Epicardial Coronary Arteries Compared With That in Left Ventricular Mass. Circulat… [cited by applicant]
Gould KL. Does coronary flow trump coronary anatomy? JACC Cardiovascular imaging 2009;2: 1009-23. [cited by applicant]
Huo Y, Wischgoll T, Choy JS et al. CT-based diagnosis of diffuse coronary artery disease on the basis of scaling power laws. Radiology 2013;268:694-701. [cited by applicant]
International Search Report and the Written Opinion ofthe International Searching Authority received in related PCT/US2016/044895, dated Oct. 26, 2016; 12 pages. [cited by applicant]
Kim, Kyung Hwan, et al. “Patient-specific coronary artery blood flow simulation using myocardial volume partitioning.” SPIE Medical Imaging. International Society for Optics and Photonics, 2013. [cited by applicant]
Le, Huy et al. “Estimation of regional myocardial mass at risk based on distal arterial lumen volume and length using 30 micro-CT images”. Comput Med Imaging Graph. 32( 6), 488-501. 2008. [cited by applicant]
Molloi et al. “Estimation of coronary artery hyperemic blood flow based on arterial lumen vol. using angiographic Images”. Int J Cardiovasc Imaging 28, 1-11, 2012. [cited by applicant]
Seiler, Christian et al. “Basic Structure-Function Relations of the Epicardial Coronary Vascular Tree”. Basis of Quantitative Coronary Arteriography for Diffuse Coronary Artery Disease, 2-18. 1987. [cited by applicant]
Takarada et al. “An angiographic technique for coronary fractional flow reserve measurement: in vivo validation”. The International journal of cardiovascular imaging, 29(3), 535-544, 2013. [cited by applicant]
Wong et al. “Quantification of fractional flow reserve based on angiographic image data”. The international journal of ; cardiovascular imaging, 28(1), 13-22, 2012. [cited by applicant]
Zhou et al., “In vivo validation of the design rules of the coronary arteries and their application in the assessment of diffuse disease” Physics in Medicine and Biology, vol. 4 7, pp. 977-993, 2002. [cited by applicant]
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US 12,440,180 US 12,499,539 US 12,555,228 US 12,558,048 US 12,599,352 US 12,635,965