Method and system for multi-component heart and aorta modeling for decision support in cardiac disease

• US 8,527,251 B2
• Filed: 04/30/2010
• Issued: 09/03/2013
• Est. Priority Date: 05/01/2009
• Status: Expired due to Fees

• Current Assignee: Siemens Healthcare GMBH (Siemens AG)
• Original Assignee: Siemens AG
• Inventors: Ionasec, Razvan Ioan, Sharma, Puneet, Georgescu, Bogdan, Torzhkov, Andrey, Moerchen, Fabian, Wittenberg, Gayle M., Fradkin, Dmitriy, Comaniciu, Dorin
• Primary Examiner(s): Thangavelu, Kandasamy
• Application Number:
  US12/770,850
• Publication Number:
  US 20100280352A1
• Time in Patent Office: 1,222 Days
• Field of Search: 703/2, 703/11, 707/104.1, 600/1, 600/466, 600/438, 600/523, 382/131, 434/262
• US Class Current: 703/11
• CPC Class Codes: A61B 5/0263 using NMR
• CPC Class Codes: G01R 33/56 Image enhancement or correction, e.g. subtraction or averaging techniques , e.g. improvement of signal-to-noise ratio and resolution
• CPC Class Codes: G01R 33/5608 Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels image data processing or generation, in general G06T
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient'"'"'s cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A multi-component patient specific anatomical heart model is generated by integrating the individual models for each of the heart components. Fluid Structure Interaction (FSI) simulations are performed on the patient specific anatomical model, and patient specific clinical parameters are extracted based on the patient specific heart model and the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.

Claims

1. A method for multi-component heart modeling and cardiac disease decision support, comprising:

receiving at least one sequence of volumetric cardiac imaging data of a patient generated using at least one medical imaging modality;

generating a multi-component patient specific 4D geometric model of the heart and aorta estimated from the at least one sequence of volumetric cardiac imaging data of the patient by;

estimating individual 4D models for a plurality of heart components in said at least one sequence of volumetric cardiac imaging data, and

generating a patient specific 4D geometrical heart model by integrating the individual 4D models for each of said plurality of heart components;

generating a patient specific 4D computational model based on one or more of personalized geometry, material properties, fluid boundary conditions, and flow velocity measurements in the 4D geometric model;

generating a patient specific biomechanical model based on Fluid Structure Interaction (FSI) simulations using the 4D computational model;

extracting patient specific clinical parameters based on the 4D geometric model and the FSI simulations; and

performing disease progression modeling and risk stratification based on the patient specific clinical parameters.

2. The method of claim 1, further comprising:

determining morphological and dynamic parameters for any of a plurality of heart components based on the 4D geometric heart model.

3. The method of claim 2, further comprising:

determining at least one hemodynamic parameter based on the biomechanical model.

4. The method of claim 3, wherein said at least one hemodynamic parameter comprises at least one of wall shear stress, compliance and stiffness, turbulence intensity, dilation pressure, and full heart blood velocity patterns.

5. The method of claim 1, wherein said step of receiving at least one sequence of volumetric cardiac imaging data of the patient generated using at least one medical imaging modality comprises:

receiving at least one of a 4D computed tomography (CT) image sequence and a 4D echocardiography image sequence, and a 4D magnetic resonance imaging (MRI) sequence.

6. The method of claim 1, wherein said step of estimating individual 4D models for a plurality of heart components in said at least one sequence of volumetric cardiac imaging data comprises:

estimating individual models of a left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk.

7. The method of claim 1, wherein said step of generating a patient specific 4D geometrical heart model by integrating the individual 4D models for each of said plurality of heart components comprises:

establishing mesh point correspondence between connecting ones of the individual models.

8. The method of claim 1, wherein:

said step of estimating individual 4D models for a plurality of heart components in said at least one sequence of volumetric cardiac imaging data comprises estimating an individual model for each of said plurality of heart components in each frame of the at least one sequence of volumetric cardiac imaging data; and

said step of generating a patient specific 4D geometrical heart model by integrating the individual 4D models for each of said plurality of heart components comprises generating a patient specific geometrical heart model in each frame of the at least one sequence of volumetric cardiac imaging data.

9. The method of claim 1, wherein said sequence of volumetric cardiac imaging data is generated over at least one heart cycle.

10. The method of claim 1, wherein said step of performing disease progression modeling and risk stratification based on the patient specific clinical parameters comprises:

generating at least one cardiac disease progression model based on the extracted clinical parameters using a multi-level Markov Cycle Tree model.

11. The method of claim 1, wherein said step of performing disease progression modeling and risk stratification based on the patient specific clinical parameters comprises:

generating at least one cardiac disease progression model having a plurality of trajectories corresponding to a plurality of alternative treatments and one or more disease complications through which the trajectories pass.

12. The method of claim 1, wherein said step of performing disease progression modeling and risk stratification based on the patient specific clinical parameters comprises:

classifying the patient as low-risk or high risk for each complication associated with one or more cardiac diseases.

13. An apparatus for multi-component heart modeling and cardiac disease decision support, comprising:

means for generating a multi-component patient specific 4D geometric model of the heart and aorta estimated from a sequence of volumetric cardiac imaging data of a patient generated using at least one medical imaging modality comprising;

means for estimating individual 4D models for a plurality of heart components in said at least one sequence of volumetric cardiac imaging data, and

means for generating a patient specific 4D geometrical heart model by integrating the individual 4D models for each of said plurality of heart components;

means for generating a patient specific 4D computational model based on one or more of personalized geometry, material properties, fluid boundary conditions, and flow velocity measurements in the 4D geometric model; and

means for generating a patient specific biomechanical model based on Fluid Structure Interaction (FSI) simulations using the 4D computational model;

means for extracting patient specific clinical parameters based on the 4D geometric model and the FSI simulations; and

means for performing disease progression modeling and risk stratification based on the patient specific clinical parameters.

14. The apparatus of claim 13, wherein said means for performing disease progression modeling and risk stratification based on the patient specific clinical parameters comprises:

means for generating at least one cardiac disease progression model based on the extracted clinical parameters using a multi-level Markov Cycle Tree model.

15. The apparatus of claim 13, wherein said means for performing disease progression modeling and risk stratification based on the patient specific clinical parameters comprises:

means for generating at least one cardiac disease progression model having a plurality of trajectories corresponding to a plurality of alternative treatments and one or more disease complications through which the trajectories pass.

16. The apparatus of claim 13, wherein said means for performing disease progression modeling and risk stratification based on the patient specific clinical parameters comprises:

means for classifying the patient as low-risk or high risk for each complication associated with one or more cardiac diseases.

17. A non-transitory computer readable medium encoded with computer executable instructions for multi-component heart modeling and cardiac disease decision support, the computer executable instructions defining steps comprising:

receiving at least one sequence of volumetric cardiac imaging data of the patient generated using at least one medical imaging modality;

generating a multi-component patient specific 4D geometric model of the heart and aorta estimated from the at least one sequence of volumetric cardiac imaging data of the patient generated using at least one medical imaging modality by;

estimating individual 4D models for a plurality of heart components in said at least one sequence of volumetric cardiac imaging data, and

generating a patient specific 4D geometrical heart model by integrating the individual 4D models for each of said plurality of heart components;

generating a patient specific 4D computational model based on one or more of personalized geometry, material properties, fluid boundary conditions, and flow velocity measurements in the 4D geometric model;

generating a patient specific biomechanical model based on Fluid Structure Interaction (FSI) simulations using the 4D computational model;

extracting patient specific clinical parameters based on the 4D geometric model and the FSI simulations; and

performing disease progression modeling and risk stratification based on the patient specific clinical parameters.

18. The computer readable medium of claim 17, wherein the computer executable instructions defining the step of performing disease progression modeling and risk stratification based on the patient specific clinical parameters comprise computer executable instructions defining the step of:

generating at least one cardiac disease progression model based on the extracted clinical parameters using a multi-level Markov Cycle Tree model.

19. The computer readable medium of claim 17, wherein the computer executable instructions defining the step of performing disease progression modeling and risk stratification based on the patient specific clinical parameters comprise computer executable instructions defining the step of:

generating at least one cardiac disease progression model having a plurality of trajectories corresponding to a plurality of alternative treatments and one or more disease complications through which the trajectories pass.

20. The computer readable medium of claim 17, wherein the computer executable instructions defining the step of performing disease progression modeling and risk stratification based on the patient specific clinical parameters comprise computer executable instructions defining the step of:

classifying the patient as low-risk or high risk for each complication associated with one or more cardiac diseases.