PATIENT-TAILORED HEMODYNAMICS ANALYSIS FOR THE PLANNING OF A HEART VALVE IMPLANTATION

Abstract

The present invention refers to a method for planning a heart valve implantation in a subject. The present invention further relates to a method for determining performance of an implanted heart valve as well as to a method for determining subject-specific blood flow characteristics for at least a portion of the heart and/or aorta. The invention further relates to a system for determining subject-specific blood flow characteristics. The instant means and method of patient-tailored hemodynamics analysis are particularly useful in preoperative planning of an implantation of an artificial heart valve in a human subject.

Claims

1. A method for planning a heart valve implantation in a subject, the method comprising: (a) providing a subject-specific three-dimensional representation of the geometry of at least a portion of the heart and/or aorta and of blood flow therein, (b) training the mathematical model based on a training dataset comprising experimental blood flow characteristics in phantoms of at least the portion of the heart and/or aorta; (c) determining blood flow characteristics using a previously trained mathematical model in the presence and absence of an implanted heart valve based on the three-dimensional representation obtained in (a) and varied size, design, implantation depth, deployment thickness and/or orientation of the implanted heart valve, and (d) determining the size, design, implantation depth, deployment thickness and orientation of the implanted heart valve based on the determined blood flow characteristics in the presence and absence of the implanted heart valve, wherein the size, design, implantation depth, deployment thickness and/or orientation of the implanted heart valve are determined to minimize pressure drop, regurgitation volume, turbulence intensity, high shear stress regions, stagnation regions and/or flow separation regions.

2. The method according to claim 1, wherein the blood flow characteristics comprise blood flow patterns, velocity, retrograde flow rate, kinetic energy, vorticity, helicity, turbulence intensity, energy loss and/or shear stress.

3. The method according to claim 1 or 2, wherein the blood flow characteristics are temporally and/or spatially resolved.

4. The method according to any one of claims 1 to 3, wherein the blood flow characteristics are determined in systolic and/or diastolic phase.

5. The method according to any one of claims 1 to 4, wherein the previously trained mathematical model is a machine learning-based method, preferably a deep learning network.

6. The method according to any one of claims 1 to 5, wherein the three-dimensional representation of the geometry of the subject's heart and/or aorta is obtained from MRI and/or CT imaging, preferably wherein the MRI and/or CT images are automatically quantified and segmented by using a machine learning-based method, preferably wherein the machine learning-based method comprises a computer vision method.

7. The method according to any one of claims 1 to 6, wherein the previously trained mathematical model relies on a training dataset comprising experimental blood flow characteristics in phantoms of at least the portion of the heart and/or aorta.

8. The method according to claim 7, wherein the training dataset comprises the blood flow characteristics obtained from one or more subject(s) and/or through simulation(s).

9. The method according to any one of claims 1 to 8, wherein the phantom of at least the portion of the heart and/or aorta is a three-dimensional model, preferably obtained using additive manufacturing techniques according to the geometry of the heart and/or aorta of a subject, preferably using elastic and/or transparent material.

10. The method according to any one of claims 1 to 9, wherein the experimental blood flow characteristics are obtained using optical imaging techniques, preferably 2D/3D Particle Imaging and/or Particle Tracking Velocimetry.

11. The method according to any one of claims 1 to 10, wherein the training dataset comprises the experimental blood flow characteristics in the phantoms of at least the portion of the heart and/or aorta, determined in the presence of pathological conditions and/or implanted medical devices.

12. A method for determining performance of an implanted heart valve, the method comprising: (a) providing a subject-specific three-dimensional representation of the geometry of at least a portion of the heart and/or aorta, (b) obtaining a subject-specific phantom of at least the portion of the heart and/or aorta, preferably using additive manufacturing techniques according to the three-dimensional representation obtained in (a), preferably using elastic and/or transparent material, (c) using optical imaging techniques to obtain experimental blood flow characteristics in the subject-specific phantom of at least the portion of the heart and/or aorta in the presence and absence of the implanted heart valve, and (d) determining performance of the implanted heart valve based on the experimental blood flow characteristics obtained in (c), wherein determined performance of the implanted heart valve comprises pressure drop, regurgitation volume, turbulence intensity, high shear stress regions, stagnation regions and/or flow separation regions.

13. The method according to claim 12, wherein the experimental blood flow characteristics comprise blood flow patterns, velocity, retrograde flow rate, kinetic energy, vorticity, helicity, turbulence intensity, energy loss and/or shear stress.

14. The method according to claim 12 or 13, wherein the experimental blood flow characteristics are temporally and/or spatially resolved.

15. The method according to any one of claims 12 to 14, wherein the experimental blood flow characteristics are determined in systolic and/or diastolic phase.

16. The method according to any one of claims 12 to 15, wherein the optical imaging techniques comprise 2D/3D Particle Imaging and/or Particle Tracking Velocimetry.

17. The method according to any one of claims 12 to 16, wherein the three-dimensional representation of the geometry of the subject's heart and/or aorta is obtained from MRI and/or CT imaging, preferably wherein the MRI and/or CT images are automatically quantified and segmented by using a machine learning-based method, preferably wherein the machine learning-based method comprises a computer vision method.

18. A method for determining subject-specific blood flow characteristics for at least a portion of the heart and/or aorta, the method comprising: (a) providing a subject-specific three-dimensional representation of the geometry of at least the portion of the heart and/or aorta and of blood flow therein; and (b) determining the blood flow characteristics based on the three-dimensional representation obtained in (a) using a previously trained mathematical model.

19. The method according to claim 18, wherein the blood flow characteristics comprise three-dimensional blood flow patterns, velocity, retrograde flow rate, kinetic energy, vorticity, helicity, turbulence intensity, stroke work, energy loss and/or shear stress.

20. The method according to claim 18 or 19, wherein the blood flow characteristics are temporally and/or spatially resolved.

21. The method according to any one of claims 18 to 20, wherein the blood flow characteristics are determined in systolic and/or diastolic phase.

22. The method according to any one of claims 18 to 21, wherein the previously trained mathematical model is a machine learning-based method, preferably a deep learning network.

23. The method according to any one of claims 18 to 22, wherein the three-dimensional representation of the geometry of the subject's heart and/or aorta is obtained from MRI and/or CT imaging, preferably wherein the MRI and/or CT images are automatically quantified and segmented by using a machine learning-based method, preferably wherein the machine learning-based method comprises a computer vision method.

24. The method according to any of claims 18 to 23, wherein the previously trained mathematical model relies on a training dataset comprising experimental blood flow characteristics in phantoms of at least the portion of the heart and/or aorta.

25. The method according to claim 24, wherein the training dataset comprises blood flow characteristics obtained from one or more subject(s) and/or through simulation(s).

26. The method according to claim 24 or 25, wherein the phantom of at least the portion of the heart and/or aorta is a three-dimensional model, preferably obtained using additive manufacturing techniques according to the geometry of the heart and/or aorta of a subject, preferably using elastic and/or transparent material.

27. The method according to any one of claims 24 to 26, wherein the experimental blood flow characteristics are obtained by using optical imaging techniques, preferably 2D/3D Particle Imaging and/or Particle Tracking Velocimetry.

28. The method according to any one of claims 24 to 27, wherein the training dataset comprises the experimental blood flow characteristics in the phantoms of at least the portion of the heart and/or aorta, determined in the presence of pathological conditions and/or implanted medical devices.

29. A system for determining subject-specific blood flow characteristics, the system comprising at least one computer configured to: (a) receive a subject-specific three-dimensional representation of the geometry of at least a portion of the heart and/or aorta, (b) optionally receive information about size, design, implantation depth, deployment thickness, and/or orientation of an implanted heart valve, and (c) determine the blood flow characteristics based on the three-dimensional representation received in (a) and optionally on size, design, implantation depth, deployment thickness, and/or orientation of the implanted heart valve received in (b) by using a previously trained mathematical model.

30. The system of claim 29, wherein the blood flow characteristics comprise blood flow patterns, velocity, retrograde flow rate, kinetic energy, vorticity, helicity, turbulence intensity, energy loss and/or shear stress.

31. The system of claim 30, wherein the blood flow characteristics are temporally and/or spatially resolved.

32. The system of claim 30 or 31, wherein the blood flow characteristics are determined in systolic and/or diastolic phase.

33. The system of any one of claims 29 to 32, wherein the previously trained mathematical model is a machine learning-based method, preferably a deep learning network, wherein preferably the deep learning network is implemented using GPU-based cloud computing.

34. The system of any one of claims 29 to 33, wherein the three-dimensional representation of the geometry of the subject's heart and/or aorta is obtained from MRI and/or CT imaging, preferably wherein the MRI and/or CT images are automatically quantified and segmented by using a machine learning-based method, preferably wherein the machine learning-based method comprises a computer vision method.

35. The system of any one of claims 29 to 34, wherein the previously trained mathematical model relies on a training dataset comprising experimental blood flow characteristics in phantoms of at least the portion of the heart and/or aorta.

36. The system of claim 35, wherein the training dataset further comprises the blood flow characteristics obtained from one or more subject(s) and/or through simulation(s).

37. The system of claim 35 or 36, wherein the phantom of at least the portion of the heart and/or aorta is a three-dimensional model, preferably obtained using additive manufacturing techniques according to the geometry of the heart and/or aorta of a subject, preferably using elastic, compliant, and/or transparent material.

38. The system of any one of claims 35 to 37, wherein the experimental blood flow characteristics in the phantoms of at least the portion of the heart and/or aorta are obtained by using optical imaging techniques, preferably 2D/3D Particle Imaging and/or Particle Tracking Velocimetry.

39. The system of any one of claims 35 to 38, wherein the training dataset comprises the experimental blood flow characteristics in the phantoms of at least the portion of the heart and/or aorta, determined in the presence of pathological conditions and/or implanted medical devices.

40. A method for planning a heart valve implantation in a subject, the method comprising: (a) providing a subject-specific three-dimensional representation of the geometry of at least a portion of the heart and/or aorta and of blood flow therein, (b) training the mathematical model based on a training dataset comprising the blood flow characteristics obtained from one or more subject(s); (c) determining blood flow characteristics using a previously trained mathematical model in the presence and absence of an implanted heart valve based on the three-dimensional representation obtained in (a) and varied size, design, implantation depth, deployment thickness and/or orientation of the implanted heart valve, and (d) determining the size, design, implantation depth, deployment thickness and/or orientation of the implanted heart valve based on the determined blood flow characteristics in the presence and absence of the implanted heart valve, wherein the size, design, implantation depth, deployment thickness and/or orientation of the implanted heart valve are determined to minimize pressure drop, regurgitation volume, turbulence intensity, high shear stress regions, stagnation regions and/or flow separation regions.

41. The method according to claim 40, wherein the blood flow characteristics comprise blood flow patterns, velocity, retrograde flow rate, kinetic energy, vorticity, helicity, turbulence intensity, energy loss and/or shear stress.

42. The method according to claim 40 or 41, wherein the blood flow characteristics are temporally and/or spatially resolved.

43. The method according to any one of claims 40 to 42, wherein the blood flow characteristics are determined in systolic and/or diastolic phase.

44. The method according to any one of claims 40 to 43, wherein the previously trained mathematical model is a machine learning-based method, preferably a deep learning network.

45. The method according to any one of items 40 to 44, wherein the three-dimensional representation of the geometry of the subject's heart and/or aorta is obtained from MRI and/or CT imaging, preferably wherein the MRI and/or CT images are automatically quantified and segmented by using a machine learning-based method, preferably wherein the machine learning-based method comprises a computer vision method.

46. The method according to any one of items 40 to 45, wherein the previously trained mathematical model relies on a training dataset comprising experimental blood flow characteristics in phantoms of at least the portion of the heart and/or aorta.

47. The method according to claim 46, wherein the phantom of at least the portion of the heart and/or aorta is a three-dimensional model, preferably obtained using additive manufacturing techniques according to the geometry of the heart and/or aorta of a subject, preferably using elastic and/or transparent material.

48. The method according to claim 46 or 47, wherein the experimental blood flow characteristics are obtained using optical imaging techniques, preferably 2D/3D Particle Imaging and/or Particle Tracking Velocimetry.

49. The method according to any one of claims 46 to 48, wherein the training dataset comprises the experimental blood flow characteristics in the phantoms of at least the portion of the heart and/or aorta, determined in the presence of pathological conditions and/or implanted medical devices.

Description

BRIEF DESCRIPTION OF FIGURES

[0281] FIG. 1 represents the workflow and its implementation used for providing a subject-specific three-dimensional representation of the geometry of at least the part of the heart and/or aorta of the present invention.

[0282] FIG. 2 represents the workflow and its implementation used for planning a heart valve implantation in a subject according to the present invention.

[0283] FIG. 3 represents the workflow and its implementation used for determining performance of an implanted heart valve according to the present invention.

[0284] FIG. 4 represents the workflow and its implementation used for determining subject-specific blood flow characteristics for at least a portion of the heart and/or aorta according to the present invention.

[0285] FIG. 5 depicts an example of an inserted prosthetic valve in the aorta. The prosthetic valve replaces the native valve of the patient.

[0286] FIG. 6 shows different valve designs in the same patient specific anatomy. Left: implanted prosthetic valve design #1, Middle: implanted prosthetic valve design #2, Right: implanted prosthetic valve design #3.

[0287] FIG. 7 depicts the orientation of the implanted heart valve in the same patient-specific anatomy. Top panel shows the orientation in the inferior view. The prosthetic valve may be placed between 0-120 degrees compared to the native valve. Top left, 0 degrees; top right, 45 degrees of implantation. Bottom panel shows the orientation on the side view. The prosthetic valve can be placed within a few 10 s of degrees in order to optimize the blood flow along the ascending aorta. Bottom left, 0 degrees; bottom right, 15 degrees.

[0288] FIG. 8 illustrates the implantation of different valve sizes for the same valve design in the same patient-specific anatomy. Left, 23 mm diameter valve; Right, 25 mm diameter valve.

[0289] FIG. 9 shows the valve implantation depths for the same patient-specific anatomy. Left, 0 mm; right, 15 mm depth of implantation with respect to the annulus.

[0290] FIG. 10 shows the valve deployment thicknesses in the same patient-specific anatomy, Left, non-expanded; Middle, partially expanded; Right, fully expanded.

[0291] FIG. 11 depicts the premilinary results of the correlation between Mean Kinetic Energy (IKE) obtained via in vivo MRI measurement and the one obtained via invitro prediction for both before the actual TAVI implantation and after the TAVI operation

[0292] Various modifications and variations of the invention will be apparent to those skilled in the art without departing from the scope of the invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention which are obvious to those skilled in the relevant fields are intended to be covered by the present invention.

Examples

Example 1

[0293] FIG. 1 represents an example of the image segmentation process in a patient-specific anatomy. The input is a stack of medical images from an MRI or CT scanner, which are converted into a pre-processed volume and are anonymized in the software. The 2D segmentation is performed based on machine learning and computer vision. The user is not permitted to continue the process without the anonymization step. The volume is segmented first using either of the two approaches: Computer vision or machine learning. While the machine learning method is much quicker, it requires powerful computers therefore the selection is left to the user. Depending on the user preferences, either cloud supported machine learning tool or offline computer vision tool provides the segmented anatomies.

[0294] The computer vision tool takes the anonymized pre-processed volumes. In this tool, initial user input is needed. The user scrolls the medical image to locate the aorta and make at least two clicks on the aorta. The processing unit takes the inputs and uses computer vision algorithms to do segmentation of full aorta (root, ascending, arch, descending), calcified regions, and valve leaflets including the stenotic region. The raw image and segmented results are stored in a database. The output (3D reconstructed aorta, calcification and stenosis measurements, and size of the aorta) are sent to the user through an interface. The user can scroll through different 2D slices and analyze the dimensions of the aorta, as well as its geometry, and calcified regions throughout the 3D volume. Additionally, an option is added for the user to prepare the geometry for 3D printing purposes.

[0295] The database from Computer Vision tool and/or manually segmented medical images is used to feed the machine learning module. This module is fully automated and does not require any user input. The user uploads the stack of medical images gathered from the medical imaging device measurement. The processing unit uses the trained model to find the anatomy needs and sends it to the database. Database sends the required anatomy as a result. The output (3D reconstructed aorta, calcification and stenosis measurements, and size of the aorta as well as other components of the heart) is stored in the database and also are sent to the user through an interface. Similar to the Computer Vision tool, the user can scroll through different 2D slices and analyze the dimensions of the aorta, as well as its geometry, and calcified regions throughout the 3D volume. Additionally, an option is added for the user to prepare the geometry for 3D printing purposes.

Example 2

[0296] FIG. 2 shows an example of the pre and post operational flow. The input is a stack of medical images from medical devices, such as CT and MRI. The images are anonymized before moving to the next steps. Deep learning algorithms segment the image, as in FIG. 1. The segmented algorithm is processed in two ways: 1) using a deep learning model created with the in-vivo 4D-MRI database and 2) using a deep learning model created using the in-vitro optical imaging database. Results of the both models calculate the fluid dynamics parameters such as velocity, retrograde flow rate, kinetic energy, vorticity, helicity, turbulence intensity, energy loss and/or shear stress. From these calculations, a risk assessment for different scenarios of implantation parameters are given to the user. These different scenarios can include implanted medical device size, orientation, and implantation depth/height.

Example 3

[0297] FIG. 3 shows an example of an in-vitro valve performance analysis. The user provides the artificial valve and information of the valve to the company. The 3D anatomy models to be tested are randomly selected from the database of patient-specific anatomies. Optical measurements are done in the selected physical model with the implanted valve. The fluid dynamics parameters from the blood flow in the selected conditions are calculated, giving the valve performance. A detailed report is sent to the user with the qualitative and quantitative information.

Example 4

[0298] FIG. 4 illustrates the example of calculation of 4D MRI flow parameters in a patient-specific anatomy. The input is a stack of medical images from CT and/or 4D MRI. A trained mathematical model segments the images, which are used in the MRI data as a mask. The velocity vector field is created in the physical coordinate system in the masked image. The output is both qualitative and quantitative flow information including velocity, retrograde flow rate, kinetic energy, vorticity, helicity, turbulent intensity, energy loss and shear stresses. The output is stored in the database, and also shared with the user through an interface. The user can analyze the flow 4D at different sections of the anatomy and can get the time-averaged, space averaged, and temporal-spatial fluid dynamics parameters.

Example 5

[0299] In the implantation procedure, the artificial heart valve is positioned on the diseased native valve. The prosthetic valve is positioned at the aortic root where the stents of the valve may extend towards the ascending aorta as shown in FIG. 5. Depending on the size and the shape of the aortic valve, the implantation parameters may vary in patient specific anatomies.

Example 6

[0300] Valve design is one of the important parameters for the heart valve replacement operations. As depicted in FIG. 6, the shape of the prosthetic valve may lead to different implantation positioning. Depending on the model, the stents, leaflets and the designs may vary and hence the positioning of the valves may vary in different anatomies.

Example 7

[0301] Orientation is one of the implantation parameters which needs to be considered during the operation. As shown in FIG. 7, the orientation of the implanted valve may vary according to the position of the diseased native valve. The technology proposed allows to identify the optimum orientation, e.g., 45 degrees or 0 degrees. Physicians can select different orientations as an input to compare, and the algorithm allows to estimate post operational blood flow parameters for the selected valve orientations.

Example 8

[0302] Valve size is another important implantation parameter. It is possible to identify the optimum size before the operation using the proposed technology. Physicians can select different sizes as an input to compare and the algorithm allows to estimate post operational blood flow parameters for the selected valve sizes.

Example 9

[0303] Valve implantation depth is another crucial implantation parameter. It is possible to identify the optimum implantation depth before the operation using the proposed technology. Physicians can select different implantation depths as an input to compare and the algorithm allows to estimate post operational blood flow parameters for the selected implantation depths.

Example 10

[0304] Valve deployment thickness is another important implantation parameter which is possible to identify the optimum valve deployment thickness before the operation using the proposed technology. Physicians can select different valve deployment thicknesses as an input to compare and the algorithm allows to estimate post operational blood flow parameters for the selected valve deployment thicknesses.

Example 11

[0305] The preliminary results of the correlation between Mean Kinetic Energy (IKE) obtained via in vivo MRI measurement and the one obtained via invitro prediction for both before the actual TAVI implantation and after the TAVI operation is shown in FIG. 11. Linear regression was utilized to define the correlation between in vivo measurements and in vitro predictions. Spatially averaged IKE values obtained via in vivo and in vitro for all phases of the heart cycle were compared. In total six patients were scanned before and after the TAVI operation. Based on their anatomical information, six different silicone models were 3D-printed. Finally in vitro optical measurements were performed using the anatomically accurate silicone models to extract the phase averaged blood velocity and IKE. The results show >92% agreemenmt for pre-TAVI and >95% agreement for post-TAVI cases.