DYNAMIC POSITIONAL INFORMATION CONSTRAINED HEART MODEL

• US 20080208068A1
• Filed: 02/26/2008
• Published: 08/28/2008
• Est. Priority Date: 02/26/2007
• Status: Abandoned Application

• Current Assignee: Proteus Biomedical, Inc. (Otsuka Holdings Company Limited)
• Original Assignee: Proteus Biomedical, Inc. (Otsuka Holdings Company Limited)
• Inventors: Robertson, Timothy, Savage, George M.
• Application Number:
  US12/037,851
• Publication Number:
  US 20080208068A1
• Time in Patent Office: Days
• US Class Current: 600/508
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Methods and systems for producing a computational model of the heart which is patient-specific are provided. Embodiments of the methods and systems include providing a computational model of a heart, obtaining dynamic positional information data from a patient and modifying the heart model with the dynamic positional information data to produce a patient-specific heart model. The invention finds use in a variety of different applications, including but not limited to diagnosis and treatment of heart conditions.

Claims

1. A method for producing a patient specific heart model that is specific for a patient, said method comprising:

(a) providing a computational model of a heart;

(b) obtaining dynamic positional information (DPI) data from said patient; and

(c) modifying said computational model with said DPI data to produce said patient specific heart model, wherein said patient specific heart model is predictive for said patient'"'"'s heart function.

2. The method according to claim 1, wherein said modifying further employs a non-DPI patient specific parameter.

3. The method according to claim 2, wherein said non-DPI patient specific parameter is a state parameter.

4. The method according to claim 3, wherein said state parameter is a drug dosing parameter.

5. The method according to claim 2, wherein said non-DPI patient specific parameter is a direct parameter.

6. The method according to claim 2, wherein said non-DPI patient specific parameter is an observed parameter.

7. The method according to claim 1, wherein said DPI data is electrical tomography data.

8. The method according to claim 1, wherein said method further comprises performing a test on said specific heart model.

9. The method according to claim 8, wherein said test comprises determining an optimal pacing site.

10. The method according to claim 8, wherein said test generates predicted cardiac performance data specific for said subject.

11. The method according to claim 10, wherein said predicted cardiac performance data is a cardiac performance score.

12. The method according to claim 10, wherein said method further comprises employing said predicted cardiac performance data in the diagnosis or treatment said patient.

13. The method according to claim 12, wherein said treatment is cardiac resynchronization therapy (CRT).

14. The method according to claim 4, wherein said direct parameter is selected from the group consisting of:

imaging data, angiographic data, EPS data, and IEGM data.

15. The method according to claim 2, wherein said parameter is selected from the group consisting of:

electrical delay time, data from pressure-volume catheters, anatomic disruption of arterial wall, aneurysmal wall thickness, atherosclerotic plaque temperature, cerebral blood flow, cerebral blood volume, CMRO2, fibrous cap thickness, flow heterogeneity, increased pulmonary interstitial markings, intraluminal hemorrhage, intravascular thrombus, ischemic brain tissue, macrophage content, mean transit time, myocardial blood flow via 13N-ammonia, myocardial contractility/function, myocardial perfusion, neuronal activation, neuronal activation CMRO2, oxygen consumption, perfusion/diffusion, perianeurysmal fibrosis, splenic tissue characterization, subendothelial lipid pool, targeted microbubble contrast agents, vascular diameter and circumference, vascular lumen diameter, vascular occlusion, ventilation/perfusion mismatch, fibrilation state, activity state, heart rate, heart rate variability, fluid status and respiration rate.

16. A system for generating a patient specific heart model that is specific for a patient, said system comprising:

(a) a computational heart model;

(b) a source of patient-specific dynamic positional information (DPI) data; and

(c) a processor configured to modify said computational heart model with patient-specific DPI data to produce said a patient specific heart model.

17. The system according to claim 16, wherein said source is a device configured to obtain patient-specific DPI data.

18. The system according to claim 16, wherein said source is a device containing patient-specific DPI data.

19. The method according to claim 16, wherein said processor is configured to modify said patient-specific heart model with a non-DPI patient specific parameter.

20. The system according to claim 16, wherein said system is configured to perform a test on said specific heart model.

21. The system according to claim 20, wherein said test comprises determining an optimal pacing site.

22. The system according to claim 20, wherein said test generates predicted cardiac performance data specific for said patient.

23. The system according to claim 22, wherein said predicted cardiac performance data is an a cardiac performance score.

24. The system according to claim 22, wherein said system is configured to employ said predicted cardiac performance data is in the diagnosis or treatment of said patient.

25. The system according to claim 24, wherein said treatment is cardiac resynchronization therapy (CRT).

26. The system according to claim 16, wherein said system further includes a visual representation of said patient specific heart model.

27. A computer readable storage medium having a processing program stored thereon, wherein said processing program operates a processor to operate a system to perform a method comprising:

(a) providing a computational model of a heart;

(b) obtaining dynamic positional information (DPI) data from said patient; and

(c) modifying said computational model with said DPI data to produce said patient specific heart model, wherein said patient specific heart model is predictive for said patient'"'"'s heart function.

28. A kit comprising programming configured to modify a computational heart model with patient-specific DPI data to generate a specific heart model in a method comprising:

(a) providing a computational model of a heart;

(b) obtaining dynamic positional information (DPI) data from said patient; and

(c) modifying said computational model with said DPI data to produce said patient specific heart model, wherein said patient specific heart model is predictive for said patient'"'"'s heart function.