Method and system for image processing to determine patient-specific blood flow characteristics
View Patent ↗Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.
1. A method of image processing for non-invasive hemodynamic assessment and electronically displaying treatment planning of a stenosis in an aorta and/or a plurality of adjacent vessels, the method comprising:
receiving non-invasively generated estimates of (1) patient-specific lumen anatomy of a patient's aorta and a plurality of adjacent vessels, and (2) patient-specific blood flow rates through the patient's aorta and the plurality of adjacent vessels, from non-invasively produced medical image data of the patient, wherein the aorta and/or the plurality of adjacent vessels exhibit one or more stenoses;
calculating patient-specific inlet and outlet boundary conditions for a computational model of blood flow based on the non-invasively generated estimates;
computing blood flow and pressure over the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels, using the computational model of blood flow and the patient-specific inlet and outlet boundary conditions, wherein computing blood flow and pressure over the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels includes simulating blood flow using a 3D computational fluid dynamics (CFD) simulation;
generating a reduced order patient-specific model of the computed blood flow and pressure over the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels;
enabling a modification of the reduced order patient-specific model to simulate treatment of the stenosis, based on the computed blood flow and pressure over the patient-specific lumen anatomy of the patient's aorta and/or the plurality of adjacent vessels; and
outputting, to an electronic storage medium or display, one or more of the reduced order patient-specific model of the computed blood flow and pressure and the modified reduced order patient-specific model simulating the treatment.
2. The method of claim 1 , further comprising:
calculating a pressure drop across a stenosis region of the aorta or an adjacent vessel using computed pressure values resulting from computing the blood flow and pressure of the aorta or the adjacent vessel.
3. The method of claim 2 , wherein calculating a pressure drop across a stenosis region of the aorta or an adjacent vessel using computed pressure values resulting from computing the blood flow and pressure of the aorta or the adjacent vessel comprises:
calculating a difference between computed pressure values before and after the stenosis region of the aorta or the adjacent vessel based on a computed flow-rate.
4. The method of claim 1 , wherein receiving non-invasively generated estimates of the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels from medical image data of a patient comprises:
detecting a vessel geometry in the medical image data for each of a plurality of parts of the aorta and/or the plurality of adjacent vessels;
initializing a respective model for each of the plurality of parts by matching one or more geometries from a library to the vessel geometry in the medical image data; and
merging the respective models for the plurality of parts to generate a final model of lumen anatomy in the aorta and/or the plurality of adjacent vessels.
5. The method of claim 1 , wherein receiving non-invasively generated estimates of the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels from medical image data of the patient comprises:
receiving non-invasively generated estimates of the patient-specific lumen anatomy of the aorta and the plurality of adjacent vessels in a contrast-enhanced magnetic resonance imaging (CE-MRI) image.
6. The method of claim 1 , wherein receiving non-invasively generated estimates of the patient-specific blood flow rates of the aorta and/or the plurality of adjacent vessels from the medical image data of the patient comprises:
receiving non-invasively generated estimates of patient-specific blood flow rates from a sequence of magnetic resonance imaging (MRI) images.
7. The method of claim 6 , wherein the sequence of magnetic resonance imaging (MRI) images are time-varying.
8. The method of claim 1 , wherein calculating patient-specific inlet and outlet boundary conditions for a computational model of blood flow based on the patient-specific lumen anatomy, the patient-specific blood flow rates of the aorta and/or the plurality of adjacent vessels, and non-invasive clinical measurements of the patient comprises:
imposing a blood flow rate estimated at an aortic root as the inlet boundary condition; and
estimating patient-specific parameters of a 3-element Windkessel model at at least one outlet of an aortic arch based on the patient-specific lumen anatomy, the patient-specific blood flow rates of the aorta and/or the plurality of adjacent vessels, and the non-invasive clinical measurements of the patient.
9. The method of claim 8 , wherein estimating patient-specific parameters of a 3-element Windkessel model at at least one outlet of the aortic arch based on the patient-specific lumen anatomy, the patient-specific blood flow rates, and the non-invasive clinical measurements of the patient comprises:
calculating mean arterial pressure (MAP) of the patient;
calculating a total resistance for each outlet based on the MAP; and
calculating resistances for each outlet based on the total resistance calculated for each outlet.
10. The method of claim 9 , wherein calculating proximal and distal resistances for each outlet based on the total resistance calculated for each outlet comprises:
calculating a proximal resistance for each outlet as a characteristic resistance of that vessel; and
calculating a distal resistance for each outlet as a difference between the total resistance for that outlet and the proximal resistance for that outlet.
11. A computer system for image processing for non-invasive hemodynamic assessment and electronically displaying treatment of a stenosis in an aorta and/or a plurality of adjacent vessels, the method comprising:
a digital storage device storing instructions that, when executed by a processor, cause the computer system to perform a method for image processing for non-invasive hemodynamic assessment and electronically displaying treatment of a stenosis in the aorta and/or the plurality of adjacent vessels; and
a processor configured to execute the instructions to perform the method for image processing for non-invasive hemodynamic assessment and electronically displaying treatment of the stenosis in the aorta and/or the plurality of adjacent vessels, the method comprising:
receiving non-invasively generated estimates of (1) patient-specific lumen anatomy of a patient's aorta and the plurality of adjacent vessels, and (2) patient-specific blood flow rates through the patient's aorta and the plurality of adjacent vessels, from non-invasively produced medical image data of the patient, wherein the aorta and/or the plurality of adjacent vessels exhibit one or more stenoses;
calculating patient-specific inlet and outlet boundary conditions for a computational model of blood flow based on the non-invasively generated estimates;
computing blood flow and pressure over the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels, using the computational model of blood flow and the patient-specific inlet and outlet boundary conditions, wherein computing blood flow and pressure over the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels includes simulating blood flow using a 3D computational fluid dynamics (CFD) simulation;
generating a reduced order patient-specific model of the computed blood flow and pressure over the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels;
enabling a modification of the reduced order patient-specific model to simulate treatment of the stenosis, based on the computed blood flow and pressure over the patient-specific lumen anatomy of the patient's aorta and plurality of adjacent vessels; and
outputting, to an electronic storage medium or display, one or more of the reduced order patient-specific model of the computed blood flow and pressure and the modified reduced order patient-specific model simulating the treatment.
12. The computer system of claim 11 , further configured for:
calculating a pressure drop across a stenosis region of the aorta or an adjacent vessel using computed pressure values resulting from computing the blood flow and pressure of the aorta or the adjacent vessel.
13. The computer system of claim 11 , wherein
receiving non-invasively generated estimates of the patient-specific blood flow rates of the aorta and/or the plurality of adjacent vessels from the medical image data of the patient comprises:
receiving non-invasively generated estimates of patient-specific blood flow rates from a sequence of magnetic resonance imaging (MRI) images.
14. The computer system of claim 11 , wherein
calculating patient-specific inlet and outlet boundary conditions for a computational model of aortic blood flow based on the patient-specific lumen anatomy, the patient-specific blood flow rates of the aorta and/or the plurality of adjacent vessels, and non-invasive clinical measurements of the patient comprises:
imposing a time-varying blood flow rate estimated at an ascending aorta as an inlet boundary condition; and
estimating patient-specific parameters of a 3-element Windkessel model at at least one outlet of an aortic arch based on the patient-specific lumen anatomy, the patient-specific blood flow rates of the aorta and/or the plurality of adjacent vessels, and the non-invasive clinical measurements of the patient.
15. The computer system of claim 14 , wherein
estimating patient-specific parameters of a 3-element Windkessel model at least one outlet of the aortic arch based on the patient-specific lumen anatomy, the patient-specific aortic blood flow rates, and the non-invasive clinical measurements of the patient comprises:
calculating mean arterial pressure (MAP) of the patient;
calculating a total resistance for each outlet based on the MAP; and
calculating resistances for each outlet based on the total resistance calculated for each outlet.
16. A non-transitory computer readable medium storing computer program instructions for image processing for non-invasive hemodynamic assessment and electronically displaying treatment of a stenosis in an aorta and/or a plurality of adjacent vessels, the computer program instructions when executed by a processor cause the processor to perform operations comprising:
receiving non-invasively generated estimates of (1) patient-specific lumen anatomy of a patient's aorta and/or the plurality of adjacent vessels, and (2) patient-specific blood flow rates through the patient's aorta and the plurality of adjacent vessels, from non-invasively produced medical image data of the patient, wherein the aorta and/or the plurality of adjacent vessels exhibit one or more stenoses;
calculating patient-specific inlet and outlet boundary conditions for a computational model of blood flow based on the non-invasively generated estimates;
computing blood flow and pressure over the patient-specific lumen anatomy of the aorta and the plurality of adjacent vessels, using the computational model of blood flow and the patient-specific inlet and outlet boundary conditions, wherein computing blood flow and pressure over the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels includes simulating blood flow using a 3D computational fluid dynamics (CFD) simulation;
generating a reduced order patient-specific model of the computed blood flow and pressure over the patient-specific lumen anatomy of the aorta and the plurality of adjacent vessels;
enabling a modification of the reduced order patient-specific model to simulate treatment of the stenosis, based on the computed blood flow and pressure over the patient-specific lumen anatomy of the patient's aorta and/or the plurality of adjacent vessels; and
outputting, to an electronic storage medium or display, one or more of the reduced order patient-specific model of the computed blood flow and pressure and the modified reduced order patient-specific model simulating the treatment.
17. The non-transitory computer readable medium of claim 16 , wherein the operations further comprise:
calculating a pressure drop across a stenosis region of the aorta or an adjacent vessel using computed pressure values resulting from computing the blood flow and pressure of the aorta or the adjacent vessel.
18. The non-transitory computer readable medium of claim 16 , wherein receiving non-invasively generated estimates of the patient-specific lumen anatomy of the aorta and/or the plurality of adjacent vessels from medical image data of a patient comprises:
detecting a vessel geometry in the medical image data for each of a plurality of parts of the aorta and/or the plurality of adjacent vessels;
initializing a respective model for each of the plurality of parts by matching one or more geometries from a library to the vessel geometry in the medical image data; and
merging the respective models for the plurality of parts to generate a final model of lumen anatomy in the aorta and/or the plurality of adjacent vessels.
19. The non-transitory computer readable medium of claim 16 , wherein receiving non-invasively generated estimates of the patient-specific blood flow rates of the aorta and/or the plurality of adjacent vessels from the medical image data of the patient comprises:
receiving non-invasively generated estimates of patient-specific blood flow rates from a sequence of magnetic resonance imaging (MRI) images.
20. The non-transitory computer readable medium of claim 19 , wherein the sequence of magnetic resonance imaging (MRI) images are time-varying.
21. The non-transitory computer readable medium of claim 16 , wherein calculating patient-specific inlet and outlet boundary conditions for a computational model of blood flow based on the patient-specific lumen anatomy, the patient-specific blood flow rates of the aorta and/or the plurality of adjacent vessels, and non-invasive clinical measurements of the patient comprises:
imposing a time-varying blood flow rate estimated at an ascending aorta as an inlet boundary condition; and
estimating patient-specific parameters of a 3-element Windkessel model at at least one outlet of an aortic arch based on the patient-specific lumen anatomy, the patient-specific blood flow rates, and the non-invasive clinical measurements of the patient.
22. The non-transitory computer readable medium of claim 21 , wherein estimating patient-specific parameters of a 3-element Windkessel model at at least one outlet of the aortic arch based on the patient-specific lumen anatomy, the patient-specific blood flow rates, and the non-invasive clinical measurements of the patient comprises:
calculating mean arterial pressure (MAP) of the patient;
calculating a total resistance for each outlet based on the MAP; and
calculating resistances for each outlet based on the total resistance calculated for each outlet.