Method for tuning patient-specific cardiovascular simulations

• US 20100017171A1
• Filed: 07/21/2008
• Published: 01/21/2010
• Est. Priority Date: 07/21/2008
• Status: Active Grant

• Current Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Original Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Inventors: Spilker, Ryan Leonard, Taylor, Chales Anthony JR.
• Granted Patent: US 8,200,466 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 2111/10 Numerical modelling
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06T 17/20 Finite element generation, e.g. wire-frame surface description, tesselation
• CPC Class Codes: G06T 19/20 Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
• CPC Class Codes: G06T 2210/24 Fluid dynamics
• CPC Class Codes: G06T 2210/41 Medical
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Computational methods are used to create cardiovascular simulations having desired hemodynamic features. Cardiovascular modeling methods produce descriptions of blood flow and pressure in the heart and vascular networks. Numerical methods optimize and solve nonlinear equations to find parameter values that result in desired hemodynamic characteristics including related flow and pressure at various locations in the cardiovascular system, movements of soft tissues, and changes for different physiological states. The modeling methods employ simplified models to approximate the behavior of more complex models with the goal of to reducing computational expense. The user describes the desired features of the final cardiovascular simulation and provides minimal input, and the system automates the search for the final patient-specific cardiovascular model.

Claims

1. A method for generating a model of a cardiovascular system, the method comprising:

a. a definition of a plurality of models of the time-varying behavior of a cardiovascular system with related parameters and a range of levels of detail,

b. a definition of objectives of the cardiovascular models including at least one of, but not limited to, features of pressure waveforms, features of flow waveforms, features of time-varying velocity fields, features of transport, features of cardiac volume, features of tissue movements, and variations in these features with changes in physiological, disease, and interventional states, and

c. a systematic tuning of the parameters defining the cardiovascular models such that at least one of these models approximately achieves the objectives.

2. The method of claim 1, wherein the tuned parameters of the cardiovascular models are related to at least one of, but are not limited to, the group consisting of resistance, impedance, compliance, inductance, cardiac chamber elastance, material parameters of tissue, vascular network structure, and changes in these parameters with changes in physiological, disease, and interventional states.

3. The method of claim 1, wherein the methods for choosing the parameters include analysis of the variations of the differences from the objectives in response to variations in the parameters.

4. The method of claim 1, wherein numerical methods are employed for at least one member of the group consisting of:

optimization, solution of a system of nonlinear equations, and solution of a nonlinear least-squares problem.

5. The method of claim 1, wherein the level of detail is increased by a change to the model including at least one member of the group consisting of:

a. a change from a model using prescribed flow in a blood vessel to a model using an elastance-based model of a cardiac chamber,

b. a change from a model that can be described by a system of ordinary differential equations that can be solved with Fourier analysis to a model that can be described by a system of ordinary differential equations that must be solved with numerical methods for temporal integration,

c. a change from a model assuming rigid vascular walls to a model assuming more realistic representations of these walls

d. an increasingly refined computational mesh, and

e. an increasingly complete polynomial basis.

6. The method of claim 1, wherein cardiovascular models with lower levels of detail are used to predict the behavior of models with higher levels of detail.

7. The method of claim 1, wherein cardiovascular models with higher levels of detail are used to increase the fidelity of models with lower levels of detail.

8. The method of claim 7, wherein the impedances that are intrinsic to pathways of flow in cardiovascular models with higher levels of detail are used to update models with lower levels of detail.

9. The method of claim 1, wherein distributed models of the cardiovascular system are used to determine relationships between lumped parameter values and changes in the distributed models.

10. The method of claim 1, wherein a plurality of measurements are made from the same cardiovascular system to determine an individual'"'"'s variability in parameters.

11. The method of claim 1, wherein a plurality of measurements are made from members of a class of patients to determine variability in parameters among that class.

12. The method of claim 1, wherein a measure of variability is used to analyze the range of potential outcomes of a treatment of a cardiovascular system.

13. A method for generating a model of a cardiovascular system, the method comprising:

a. a definition of a model of the time-varying behavior of a cardiovascular system,

b. a definition of objectives of the cardiovascular models including at least one of, but not limited to, features of pressure waveforms, features of flow waveforms, features of time-varying velocity fields, features of transport, features of cardiac volume, features of tissue movements, and variations in these features with changes in physiological, disease, and interventional states,

c. a definition of the same number of tuned parameters as objectives, and

d. a systematic tuning of the parameters defining the cardiovascular model such that it approximately achieves the objectives.

14. The method of claim 13, wherein the tuned parameters of the cardiovascular model are related to at least one member of the group consisting of, but are not limited to, resistance, impedance, compliance, inductance, cardiac chamber elastance, material parameters of tissue, vascular network structure, and changes in these parameters with changes in physiological, disease, and interventional states.

15. The method of claim 13, wherein the methods for choosing the parameters include analysis of the variations of the differences from the objectives in response to variations in the parameters.

16. The method of claim 13, wherein numerical methods are employed for at least one member of the group consisting of:

optimization, solution of a system of nonlinear equations, and solution of a nonlinear least-squares problem.

17. The method of claim 13, wherein a plurality of measurements are made from the same cardiovascular system to determine an individual'"'"'s variability in parameters.

18. The method of claim 13, wherein a plurality of measurements are made from members of a class of patients to determine variability in parameters among that class.

19. The method of claim 13, wherein a measure of variability is used to analyze the range of potential outcomes of a treatment of a cardiovascular system.