IP Library › Granted Patent US 12,201,367

Granted Patent B2

US 12,201,367 · App. 17/805,533 · Granted Jan 21, 2025

Systems and methods for predictive heart valve simulation

Inventors: Lakshmi Dasi (Dublin, OH); Amirsepehr Azimian (Columbus, OH)

Assignee: Ohio State Innovation Foundation

A61B34/10 A61B5/0044 A61B5/0263 A61B5/7275 A61B6/03 A61B6/466 A61B6/503 A61B6/507 A61B6/5217 A61B8/065 A61B8/466 A61B8/483 A61B8/5223 A61F2/2427 G06T17/20 G16H30/40 G16H50/50 A61B6/504 A61B8/0883 A61B2034/104 A61B2034/105 A61B2034/108 G06T2210/41

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Quick Facts

Patent No.: US 12,201,367
App. No.: 17/805,533
Granted: Jan 21, 2025
Kind: B2

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Abstract

Systems and methods are described herein for predictive heart valve simulation. The systems and methods described herein can include segmenting anatomical region of a heart of a patient from image data characterizing the heart of the patient. Anatomical model data that can include three-dimensional shapes of the anatomical regions of the heart can be generated based on the image data. The anatomical model data can be used to generate anatomical model data. The analytical model data can include a three-dimensional mesh of the anatomical regions of the heart. A deformed analytical model that can be indicative of a deformed position of the anatomical regions of the heart and a deformed position of the surgical object can be generated based on the analytical model data.

Claims (115)

1. A computer implemented method to predict at least one complication prior to a deployment of a surgical object into a heart or blood vessels of a patient, the method comprising:

executing, by at least a processor, program code stored in a non-transitory computer-readable-medium to perform a simulation in responding to a selected deployment configuration of potential deployment configurations, the simulation comprising:

generating first anatomical model data based on image data characterizing anatomical regions of the heart or blood vessels, wherein the first anatomical model data comprise three-dimensional structures of the anatomical regions of the heart or blood vessels in the vicinity of interaction with the surgical object in the deployment;

generating first analytical model data based on the first anatomical model data, wherein the first analytical model data comprise a three-dimensional mesh and parametric measurements of the anatomical regions of the heart or blood vessels in the vicinity of interaction with the surgical object in the deployment;

generating, using a numerical analysis engine, a first deformed analytical model based on the first analytical model data and based on a three-dimensional mesh of a virtually implanted surgical object, wherein the first deformed analytical model is indicative of a first deformed position of the anatomical regions of the heart or blood vessels and a first deformed position of the virtually implanted surgical object in the deployment, wherein the generating the first deformed analytical model comprising:

calculating a first set of size measurements of the virtually implanted surgical object in the deployment using a parametric model of the virtually implanted surgical object, wherein the first set of size measurements comprise respective gap sizes, a 2 D 1 and a 2 D, each corresponding to a two-dimensional distance between a tip of a coronary leaflet and a coronary ostium of a coronary artery; and

calculating a second set of size measurements of the virtually implanted surgical object in the deployment based on the first deformed analytical model, wherein the second set of size measurements comprise respective gap sizes, a 3 D 1 and a 3 D 2 , each corresponding to a shortest three-dimensional distance between the coronary ostium of the coronary artery and a potential obstruction;

establishing, from a database of image data from patients with and without at least one complication after having implanted the surgical object, a patient-specific predictive criteria of confirmed at least one complication comprising coronary obstruction, by:

performing systematic data-fitting on at least the first and second sets of size measurements, in combination with evaluating variables comprising one or more of: surgical object size and type, parametric measurements from the first deformed analytical model data and the first anatomical model data; and

determining a statistical correlation, R 2 , based on the systematic data-fitting; and

predicting a likelihood of the at least one complication comprising coronary obstruction; based on the patient-specific predictive criteria comprising the statistical correlation R 2 .

2. The computer implemented method of claim 1 , wherein the deployment is a partial deployment.

3. The computer implemented method of claim 1 , wherein the deployment is a full deployment.

4. The computer implemented method of claim 1 , wherein the first anatomical model data comprise three-dimensional shapes of the anatomical regions of the heart or blood vessels in the vicinity of interaction with a previously implanted surgical object.

5. The computer implemented method of claim 1 , further comprising displaying, by a display device, the first deformed analytical model.

6. The computer implemented method of claim 5 , wherein the display device is a virtual reality device.

7. The computer implemented method of claim 5 , wherein the displaying comprises providing visualization of the first deformed analytical model for the selected deployment configuration.

8. The computer implemented method of claim 5 , wherein the displaying comprises visualization with color coding.

9. The computer implemented method of claim 1 , further comprising:

determining sensitivity of the surgical object relative to the patient's anatomy by:

generating a plurality of deformed analytical models based on the first analytical model data and based on the three-dimensional mesh of the virtually implanted surgical object, wherein each of the plurality of deformed analytical models is indicative of the sensitivity of the anatomical regions of the heart or blood vessels and the respective plurality of deformed positions of the virtually implanted surgical object, wherein the generating of the plurality of deformed analytical model comprising:

selecting different deployment configurations of the potential deployment configurations;

predicting the likelihood of the at least one complication for each of the different deployment configurations using the predictive criteria established from the database of image data; and

quantifying the sensitivity as the patient's risk of the at least one complication with respect to variability in the selected deployment configurations and material properties.

10. The computer implemented method of claim 9 , further comprising providing visual feedback of the patient's risk of the at least one complication prior to implanting the surgical object.

11. The computer implemented method of claim 1 , wherein the surgical object comprises a surgical bioprosthetic heart valve, a trans-catheter heart valve, an artificial root, a surgical instrument, or a stent graft.

12. The computer implemented method of claim 1 , wherein the generating first anatomical model data comprises segmenting image data based on shape recognition of different anatomical regions of the heart or blood vessels comprising aortic root, native aortic leaflets, and/or calcific nodules.

13. The computer implemented method of claim 1 , further comprising establishing the patient-specific predictive criteria based on the first and second sets of measurements normalized with respect to an anatomical distance of the patient.

14. The computer implemented method of claim 1 , wherein the first and second sets of measurements comprise sizes, shapes, and/or positions of calcific modules.

15. The computer implemented method of claim 1 , wherein the first deformed analytical model data further correspond to the virtually implanted surgical object in the selected deployment configuration of the potential deployment configurations comprising different depths, yaw, and pitch angles relative to one of the anatomical regions of the heart or blood vessels where the surgical object is being implanted.

16. The computer implemented method of claim 1 , wherein the potential obstruction comprises a calcific nodule and/or an aortic leaflet.

17. A system for predictive simulation of a surgical object deployment into a heart or blood vessels of a patient, comprising:

at least one processor;

a non-transitory computer readable medium having stored thereon, a computer program having at least one code section for predicting complications prior to a deployment of the surgical object into the heart or blood vessels of the patient, the at least one code section being executable by the at least one processor, causing the system to perform simulations in responding to a selected deployment configuration of potential deployment configurations, the simulations comprising steps of:

establishing, from a database of image data from patients with and without the at least one complication after having implanted the surgical object, an optimal a patient-specific predictive criteria of confirmed at least one complication comprising coronary obstruction, by:

performing systematic data-fitting on at least one of the first and second sets of size measurements, in combination with evaluating variables comprising one or more of: surgical object size and type, parametric measurements from the first deformed analytical model data and the first anatomical model data; and

determining a statistical correlation, R 2 , based on the systematic data-fitting; and

predicting a likelihood of the at least one complication comprising coronary obstruction based on the patient-specific predictive criteria comprising the statistical correlation R 2 .

18. The system of claim 17 , further comprising a display device capable of displaying results of the predictive simulation.

19. The system of claim 18 , wherein the display device is a virtual reality device.

20. The system of claim 17 , wherein the steps further comprise:

determining sensitivity of the surgical object relative to the patient's anatomy by:

selecting different deployment configurations of the potential deployment configurations;

predicting the likelihood of the at least one complication for each of the different deployment configurations using the predictive criteria established from the database of image data; and

quantifying the sensitivity as the patient's risk of the at least one complication with respect to variability in the selected deployment configurations and material properties.

21. The system of claim 17 , wherein the steps further comprising establishing the patient-specific predictive criteria based on the first and second sets of measurements normalized with respect to an anatomical distance of the patient, and the first and second sets of measurements further comprise sizes, shapes, and/or positions of calcific modules.

22. The system of claim 17 , wherein the first deformed analytical model data further correspond to the virtually implanted surgical object in the selected deployment configuration of the potential deployment configurations comprising different depths, yaw, and pitch angles relative to one of the anatomical regions of the heart or blood vessels where the surgical object is being implanted.

23. A computer implemented method to predict at least one complication prior to a deployment of a surgical object into a heart or blood vessels of a patient, the method comprising:

executing, by at least a processor, program code stored in a non-transitory computer-readable-medium to perform a simulation, comprising:

generating a first set of stress and blood flow measurements based on the first deformed model, using the numerical analysis engine with algorithms capable of performing solid and fluid analysis comprising finite element analysis and computation fluid dynamics, wherein the first set of stress and blood flow measurements are indicative of modeled blood flow properties in the vicinity of interaction with the virtually implanted surgical object in the deployment;

establishing, from a database of image data from patients with and without the at least one complication after having implanted the surgical object, an optimal patient-specific predictive criteria of confirmed at least one complication comprising coronary obstruction, by:

performing systematic data-fitting on at least of the first and second sets of size measurements and the first set of stress and blood flow measurements, in combination with evaluating variables comprising one or more of: surgical object size and type, parametric measurements from the first deformed analytical model data and the first anatomical model data; and

determining a statistical correlation, R 2 , based on the systematic data-fitting; and

predicting a likelihood of the at least one complication comprising coronary obstruction based on the patient-specific predictive criteria comprising the statistical correlation R 2 .

24. The computer implemented method of claim 23 , wherein the blood flow measurements comprise velocity, pressure gradient, leakage, stasis zones, stress distribution, or a combination thereof.

25. The computer implemented method of claim 23 , further comprising predicting the likelihood of thrombosis with the optimal patient-specific predictive criteria.

26. The computer implemented method of claim 23 , further comprising predicting the likelihood of paravalvular leakage with the optimal patient-specific predictive criteria.

27. The computer implemented method of claim 23 , further comprising predicting the likelihood of conduction abnormalities with the optimal patient-specific predictive criteria.

28. The computer implemented method of claim 23 , further comprising predicting the likelihood of cerebrovascular events with the optimal patient-specific predictive criteria.

29. The computer implemented method of claim 23 , further comprising:

determining sensitivity of the surgical object relative to the patient's anatomy by:

generating a plurality of deformed analytical models based on the first analytical model data and based on the three-dimensional mesh of the virtually implanted surgical object; and

generating a plurality sets of blood flow measurements based on the plurality of deformed analytical models, wherein each of the plurality of deformed analytical models and each of the plurality sets of blood flow measurements are indicative of the sensitivity of the anatomical regions of the heart or blood vessels and the respective plurality of deformed positions of the virtually implanted surgical object, wherein the generating of the plurality of deformed analytical models and the plurality sets of blood flow measurements comprising:

varying deployment configurations of the surgical object comprising different depths, yaw, and pitch angles relative to one of the anatomical regions of the heart or blood vessels where the surgical object is being implanted;

predicting the likelihood of the at least one complication for each configuration using the predictive criteria established from the database of image data; and

quantifying the sensitivity as the patient's risk of the at least one complication with respect to variability in the deployment configurations and material properties.

30. The computer implemented method of claim 29 , further comprises providing visual feedback of the patient's risk of the at least one complication prior to implanting the surgical object.

31. The computer implemented method of claim 23 , further comprising displaying, by a display device, the first deformed analytical model and the first set of stress and blood flow measurements for selected deployment configurations prior to implanting the surgical object.

32. The computer implemented method of claim 23 , wherein the surgical object comprises a surgical bioprosthetic heart valve, a trans-catheter heart valve, an artificial root, a surgical instrument, or a stent graft.

33. The computer implemented method of claim 23 further comprising

displaying, by a display device, the first deformed analytical model.

34. The computer implemented method of claim 33 , further comprising

displaying, by the display device, the first deformed analytical model comprising the first set of stress and blood flow measurements.

35. The computer implemented method of claim 34 , wherein the displaying comprises providing visualization of the first deformed analytical model comprising the first set of stress and blood flow measurements for the selected deployment configuration.

36. The computer implemented method of claim 34 , wherein the displaying comprises in responding to a selected surgical object size and type, providing visualization of the first deformed analytical model comprising the first set of stress and blood flow measurements.

37. The computer implemented method of claim 34 , wherein the displaying comprises in responding to the selected deployment configuration, displaying visualization of the first deformed analytical model with respective orientation relative to a three-dimensional image of the patient.

38. The computer implemented method of claim 33 , wherein the displaying comprises visualization with parametric color coding.

39. The computer implemented method of claim 33 , wherein the displaying comprises providing visualization of real time comparisons of selected types, sizes, and/or deployment configurations of the surgical object.

40. A system for predictive simulation of a surgical object deployment into a heart or blood vessels of a patient, comprising:

at least one processor;

a non-transitory computer readable medium having stored thereon, a computer program having at least one code section for predicting complications prior to a deployment of a surgical object into a heart or blood vessels of a patient, the at least one code section being executable by the at least one processor, causing the system to perform simulations in responding to a selected deployment configuration of potential deployment configurations, the simulations comprising the steps of:

performing systematic data-fitting on at least one of the first and second sets of size measurements and the first set of stress and blood flow measurements, in combination with evaluating variables comprising one or more of: surgical object size and type, parametric measurements from the first deformed analytical model data and the first anatomical model data; and

determining a statistical correlation, R 2 , based on the systematic data-fitting; and

predicting a likelihood of the at least one complication comprising coronary obstruction based on the patient-specific predictive criteria comprising the statistical correlation R 2 .

41. The system of claim 40 , further comprising a display device configured to display the first deformed analytical model comprising the first set of stress and blood flow measurements for selected employment deployment configurations prior to implanting the surgical object.

42. A computer implemented method to predict at least one complication prior to a deployment of a surgical object into a heart or blood vessels of a patient, the method comprising:

generating first anatomical model data by segmentation of image data characterizing anatomical regions of the heart or blood vessels, wherein the first anatomical model data comprise three-dimensional structures of the anatomical regions of the heart or blood vessels in the vicinity of interaction with the surgical object in the deployment;

determining a statistical correlation, R 2 , based on the systematic data-fitting; and

predicting a likelihood of the at least one complication comprising coronary obstruction based on the patient-specific predictive criteria comprising the statistical correlation R 2 .

43. The computer implemented method of claim 42 , wherein the simulation comprises making the segmentation of image data based on one or more of: shape recognition, thresholding, edge detection, filtering, and clustering.

Assignments (1)

ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 6, 2022

From: DASI, LAKSHMI; AZIMIAN, AMIRSEPEHR

To: OHIO STATE INNOVATION FOUNDATION

Reel/Frame 060109/0273 →

Continuity (3)

Continuation 16335614

Provisional Application 62403940 · Oct 4, 2016

Related Publication 20220296305A1 · Sep 22, 2022

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