Method and System for Advanced Measurements Computation and Therapy Planning from Medical Data and Images Using a Multi-Physics Fluid-Solid Heart Model

• US 20130197884A1
• Filed: 02/01/2013
• Published: 08/01/2013
• Est. Priority Date: 02/01/2012
• Status: Active Grant

• Current Assignee: Siemens Healthineers AG (Siemens AG)
• Original Assignee: Siemens Corp. (Siemens AG)
• Inventors: Mansi, Tommaso, Mihalef, Viorel, Georgescu, Bogdan, Sharma, Puneet, Kamen, Ali, Comaniciu, Dorin, Zheng, Xudong, Rapaka, Saikiran
• Granted Patent: US 9,129,053 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: A61B 8/469 for selection of a region of interest
• CPC Class Codes: A61B 8/483 involving the acquisition of a 3D volume of data
• CPC Class Codes: G06T 19/00 Manipulating 3D models or images for computer graphics
• CPC Class Codes: G06T 2210/41 Medical
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Method and system for computation of advanced heart measurements from medical images and data; and therapy planning using a patient-specific multi-physics fluid-solid heart model is disclosed. A patient-specific anatomical model of the left and right ventricles is generated from medical image patient data. A patient-specific computational heart model is generated based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data. The computational model includes biomechanics, electrophysiology and hemodynamics. To generate the patient-specific computational heart model, initial patient-specific parameters of an electrophysiology model, initial patient-specific parameters of a biomechanics model, and initial patient-specific computational fluid dynamics (CFD) boundary conditions are marginally estimated. A coupled fluid-structure interaction (FSI) simulation is performed using the initial patient-specific parameters, and the initial patient-specific parameters are refined based on the coupled FSI simulation. The estimated model parameters then constitute new advanced measurements that can be used for decision making.

Claims

1. A method for computing advanced cardiac measurements from clinical data and medical images and performing therapy planning based on a patient-specific multi-physics heart model, the method comprising:

generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient;

generating a patient-specific computational heart model, which couples a biomechanical model, an electrophysiology model and a hemodynamic model together, based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data by;

determining initial patient-specific parameters of an electrophysiology model,

determining initial patient-specific parameters of a biomechanics model,

determining initial patient-specific computational fluid dynamics (CFD) boundary conditions,

performing a coupled fluid-structure interaction (FSI) simulation using the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions, and

refining the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions based on the coupled FSI simulation, wherein the refined parameters define a set of advanced cardiac measurements; and

simulating at least one cardiac therapy using the patient-specific computational heart model.

2. The method of claim 1, wherein the step of generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient comprises:

detecting a patient-specific left ventricle model and a patient-specific right ventricle model in the medical image data; and

fusing the left ventricle model and the right ventricle model into a single bi-ventricular volumetric mesh.

3. The method of claim 2, wherein the step of generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient further comprises:

mapping spatial information corresponding to at least one of scars, grey zones, or fibrosis identified from an MR sequences onto a tetrahedral representation of bi-ventricular volumetric mesh.

4. The method of claim 3, wherein the MR sequence is one of a delayed enhanced MR sequence or a T1 weighted MR sequence.

5. The method of claim 2, wherein the step of generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient further comprises:

registering a tensor field of an in-vivo diffusion tensor magnetic resonance image of cardiac fibers to the bi-ventricular volumetric mesh.

6. The method of claim 2, wherein the step of generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient further comprises:

generating a model of fiber architecture based on the bi-ventricular volumetric mesh.

7. The method of claim 1, wherein the step of determining initial patient-specific parameters of an electrophysiology model comprises:

determining the initial patient-specific parameters of the electrophysiology model based on an inverse problem method that relies on an uncoupled electrophysiology solver and patient-specific electrophysiology data.

8. The method of claim 7, wherein the step of determining the initial patient-specific parameters of the electrophysiology model based on an inverse problem method that relies on an uncoupled electrophysiology solver and patient-specific electrophysiology data comprises:

adjusting a tissue diffusivity parameter of the electrophysiology model based on measured ECG data of the patient; and

adjusting a parameter controlling local tissue diffusivity and an action potential duration of the electrophysiology model based on patient-specific endocardial mappings.

9. The method of claim 1, wherein the step of determining initial patient-specific parameters of a biomechanics model comprises:

simulating motion of the patient-specific anatomical model using a computational solid mechanics (CSM) solver coupled with the electrophysiology model with the determined initial patient-specific parameters and a lumped hemodynamics model; and

determining the initial patient-specific parameters of the biomechanics model based on an inverse problem method that compares simulated cardiac motion and cardiac motion measurements performed on dynamic medical images.

10. The method of claim 9, wherein the step of determining the initial patient-specific parameters of the biomechanics model based on an inverse problem method that compares simulated cardiac motion and cardiac motion measurements performed on dynamic medical images comprises:

estimating at least one of tissue stiffness, active stress, relaxation or contraction rates.

11. The method of claim 1, wherein the step of determining initial patient-specific computational fluid dynamics (CFD) boundary conditions comprises:

simulating blood flow using a CFD solver coupled to an electro-mechanical model with the determined initial patient-specific parameters of the electrophysiology and the initial determined patient-specific parameters of the biomechanics model; and

determining the initial CFD boundary conditions based on an inverse problem method that compares the simulated blood flow with blood flow measurements in the clinical data and medical images.

12. The method of claim 1, wherein the step of performing a coupled fluid-structure interaction (FSI) simulation using the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions comprises, at each time t:

calculating a trans-membrane voltage v(t+dt) at a next time t+dt using the initial patient-specific parameters of the electrophysiology model;

calculating an active force F_c(t+dt) for the next time based on the trans-membrane voltage v(t+dt) using the initial patient-specific parameters of the biomechanics model;

calculating positions U(t+dt) of mesh points of the patient-specific anatomical model for the next time based on the active force F_c(t+dt) and under a current pressure F^p(t) using the initial patient-specific parameters of the biomechanics model.

calculating blood flow for the next time through a CFD solver based on displacements U(t+dt)−

U(t) and velocities (U(t+dt)−

U(t))/dt of the mesh points of the patient-specific anatomical model using the initial patient-specific CFD boundary conditions, wherein the blood flow simulation calculates a pressure F_p(t+dt) for the next time.

13. The method of claim 1, wherein the step of refining the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions based on the coupled FSI simulation comprises:

refining the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions based on a multivariate inverse problem method that compares the simulation results of the coupled FSI simulation in terms of cardiac motion and blood flow with the cardiac motion and blood flow measured from the clinical data, to minimize the discrepancy between the simulations results and the clinical data.

14. The method of claim 1, wherein the step of simulating at least one cardiac therapy using the patient-specific computational heart model comprises:

perturbing at least one of the patient-specific anatomical model or the patient-specific computational heart model to represent the at least one cardiac therapy; and

performing an FSI simulation of heart function and blood flow based on the perturbed at least one of the patient-specific anatomical model or the patient-specific computational heart model using the refined patient-specific parameters for the electrophysiology model, biomechanics model, and CFD boundary conditions.

15. The method of claim 14, further comprising:

calculating at least one clinical cardiac parameter based on the simulated heart function and blood flow to represent effect of the simulated at least one cardiac therapy.

16. The method of claim 15, wherein the step of calculating at least one clinical cardiac parameter based on the simulated heart function and blood flow to represent effect of the simulated at least one cardiac therapy comprises:

calculating at least one of ejection fraction, blood flow velocity, blood flow vorticity, stroke volume, electrophysiology parameters, regurgitations based on the simulated heart function and blood flow.

17. The method of claim 1, further comprising:

outputting the set of advanced cardiac measurements.

18. An apparatus for computing advanced cardiac measurements from clinical data and medical images and performing therapy planning based on a patient-specific multi-physics heart model, the method comprising:

means for generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient;

means for generating a patient-specific computational heart model, which couples a biomechanical model, an electrophysiology model and a hemodynamic model together, based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data, comprising;

means for determining initial patient-specific parameters of an electrophysiology model,

means for determining initial patient-specific parameters of a biomechanics model,

means for determining initial patient-specific computational fluid dynamics (CFD) boundary conditions,

means for performing a coupled fluid-structure interaction (FSI) simulation using the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions, and

means for refining the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions based on the coupled FSI simulation, wherein the refined parameters define a set of advanced cardiac measurements; and

means for simulating at least one cardiac therapy using the patient-specific computational heart model.

19. The apparatus of claim 18, wherein the means for determining initial patient-specific parameters of an electrophysiology model comprises:

means for determining the initial patient-specific parameters of the electrophysiology model based on a comparison of cardiac electrophysiology simulation results using uncoupled electrophysiology solver with the patient-specific clinical data.

20. The apparatus of claim 18, wherein the means for determining initial patient-specific parameters of a biomechanics model comprises:

means for simulating motion of the patient-specific anatomical model using a computational solid mechanics (CSM) solver coupled with the electrophysiology model with the determined initial patient-specific parameters and a lumped hemodynamics model; and

means for determining the initial patient-specific parameters of the biomechanics model based on a comparison of the simulated motion of the patient-specific anatomical model with patient-specific dynamic medical images.

21. The apparatus of claim 18, wherein the means for determining initial patient-specific computational fluid dynamics (CFD) boundary conditions comprises:

means for simulating blood flow using a CFD solver coupled to an electro-mechanical model with the determined initial patient-specific parameters of the electrophysiology and the initial determined patient-specific parameters of the biomechanics model; and

means for determining the initial CFD boundary conditions based on a comparison of the simulated blood flow with the clinical data.

22. The apparatus of claim 18, wherein the means for refining the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions based on the coupled FSI simulation comprises:

means for refining the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions based on a comparison of FSI simulation results in terms of cardiac motion and blood flow with the clinical data to minimize a difference between simulated cardiac motion and blood flow and observed cardiac motion and blood flow in the clinical data.

23. The apparatus of claim 18, wherein the means for simulating at least one cardiac therapy using the patient-specific computational heart model comprises:

means for perturbing at least one of the patient-specific anatomical model or the patient-specific computational heart model to represent the at least one cardiac therapy; and

means for performing an FSI simulation of heart function and blood flow based on the perturbed at least one of the patient-specific anatomical model or the patient-specific computational heart model using the refined patient-specific parameters for the electrophysiology model, biomechanics model, and CFD boundary conditions.

24. A non-transitory computer readable medium storing computer program instructions for computing advanced cardiac parameters and planning therapies using a patient-specific multi-physics heart model, the computer program instructions, when executed on a processor, cause the processor to perform operations comprising:

generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient;

generating a patient-specific computational heart model, which comprises an electrophysiology model, a biomechanics model and a hemodynamic model, based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data by;

determining initial patient-specific parameters of an electrophysiology model,

determining initial patient-specific parameters of a biomechanics model,

determining initial patient-specific computational fluid dynamics (CFD) boundary conditions,

performing a coupled fluid-structure interaction (FSI) simulation using the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions, and

refining the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions based on the coupled FSI simulation, wherein the refined parameters define a set of advanced cardiac measurements; and

simulating at least one cardiac therapy using the patient-specific computational heart model.

25. The non-transitory computer readable medium of claim 24, wherein the operation of determining initial patient-specific parameters of an electrophysiology model comprises:

determining the initial patient-specific parameters of the electrophysiology model based on an inverse problem method that compares cardiac electrophysiology simulation results obtained using an uncoupled electrophysiology solver with the patient-specific clinical data.

26. The non-transitory computer readable medium of claim 24, wherein the operation of determining the initial patient-specific parameters of the electrophysiology model based on an inverse problem method that compares cardiac electrophysiology simulation results obtained using an uncoupled electrophysiology solver with the patient-specific clinical data comprises:

adjusting a tissue diffusivity parameter of the electrophysiology model based on measured ECG data of the patient; and

adjusting a parameter controlling local tissue diffusivity and action potential duration of the electrophysiology model based on patient-specific endocardial mappings.

27. The non-transitory computer readable medium of claim 24, wherein the operation of determining initial patient-specific parameters of a biomechanics model comprises:

simulating motion of the patient-specific anatomical model using a computational solid mechanics (CSM) solver coupled with the electrophysiology model with the determined initial patient-specific parameters and a lumped hemodynamics model; and

determining the initial patient-specific parameters of the biomechanics model based on an inverse problem method that compares the simulated motion of the patient-specific anatomical model with the cardiac motion measured from patient-specific dynamic medical images.

28. The non-transitory computer readable medium of claim 27, wherein the operation of determining the initial patient-specific parameters of the biomechanics model based on an inverse problem method that compares the simulated motion of the patient-specific anatomical model with the cardiac motion measured from patient-specific dynamic medical images comprises:

estimating at least one of tissue stiffness, active stress, relaxation, or contraction rates

29. The non-transitory computer readable medium of claim 24, wherein the operation of determining initial patient-specific computational fluid dynamics (CFD) boundary conditions comprises:

simulating blood flow using a CFD solver coupled to an electro-mechanical model with the determined initial patient-specific parameters of the electrophysiology and the initial determined patient-specific parameters of the biomechanics model; and

determining the initial CFD boundary conditions based on an inverse problem method that compares the simulated blood flow with blood flow measurements in the clinical data and medical images.

30. The non-transitory computer readable medium of claim 24, wherein the operation of performing a coupled fluid-structure interaction (FSI) simulation using the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions comprises, at each time t:

calculating a trans-membrane voltage v(t+dt) at a next time t+dt using the initial patient-specific parameters of the electrophysiology model;

calculating an active force F_c(t+dt) for the next time based on the trans-membrane voltage v(t+dt) using the initial patient-specific parameters of the biomechanics model;

calculating positions U(t+dt) mesh points of the patient-specific anatomical model for the next time based on the active force F_c(t+dt) and under a current pressure F_p(t) using the initial patient-specific parameters of the biomechanics model.

calculating blood flow for the next time through a CFD solver based on displacements U(t+dt)−

U(t) and velocities (U(t+dt)−

U(t))/dt of the mesh points of the patient-specific anatomical model using the initial patient-specific CFD boundary conditions, wherein the blood flow simulation calculates a pressure F_p(t+dt) for the next time.

31. The non-transitory computer readable medium of claim 24, wherein the operation of refining the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions based on the coupled FSI simulation comprises:

refining the initial patient-specific parameters of the electrophysiology model, the initial patient-specific parameters of the biomechanics model, and the initial patient-specific CFD boundary conditions based on a multivariate inverse problems that compares simulation results of the coupled FSI simulation with the clinical data in terms of cardiac motion and blood flow.

32. The non-transitory computer readable medium of claim 24, wherein the operation of simulating at least one cardiac therapy using the patient-specific computational heart model comprises:

perturbing at least one of the patient-specific anatomical model or the patient-specific computational heart model to represent the at least one cardiac therapy; and

performing an FSI simulation of heart function and blood flow based on the perturbed at least one of the patient-specific anatomical model or the patient-specific computational heart model using the refined patient-specific parameters for the electrophysiology model, biomechanics model, and CFD boundary conditions.

33. The non-transitory computer readable medium of claim 32, wherein the operations further comprise:

calculating at least one clinical cardiac parameter based on the simulated heart function and blood flow to represent effect of the simulated at least one cardiac therapy.