Model based reconstruction of the heart from sparse samples

1. A method, comprising the steps of:

defining, by a computer processor, a parametric model representing a shape of a portion of a heart;

constructing, by the computer processor, a statistical prior of the shape from a dataset of other instances of the portion, the dataset including the portions of hearts from a plurality of patients, and wherein constructing the statistical prior comprises preparing segmented data meshes;

fitting, by the computer processor, the parametric model to the statistical prior, wherein fitting the parametric model comprises computing anatomic features from the data meshes and computing correlation coefficients among different ones of the anatomic features;

inserting a probe into a living subject, the probe having a mapping electrode;

urging the mapping electrode into contacting relationships with tissue in a plurality of locations in the portion of the heart of the subject;

acquiring electrical data from the respective locations;

relating the electrical data to the fitted parametric model to produce an isosurface of the portion of the heart of the subject; and

reconstructing the shape of the portion of the heart of the subject responsively to relating the electrical data.

2. The method according to claim 1 , wherein the parametric model has internal coordinates, and wherein defining a parametric model comprises:

representing the shape as a field function that is defined at points within a bounding domain; and

transforming the points to the internal coordinates to define transformed points.

3. The method according to claim 2 , wherein the field function has a value and radial derivatives, further comprising computing the parametric model by computing boundary conditions on the value and the radial derivatives.

4. The method according to claim 2 , wherein the parametric model comprises a spherical harmonic expansion having powers and coefficients, further comprising extending a solution of the Laplace equation by addition of new powers and new coefficients.

5. The method according to claim 2 , wherein the bounding domain comprises a unit sphere.

6. The method according to claim 2 , wherein transforming the points comprises applying a skewing transformation.

7. The method according to claim 2 , wherein transforming the points comprises applying a spherical projection transformation.

8. The method according to claim 2 , wherein transforming the points comprises applying a stretching transformation.

9. The method according to claim 2 wherein the transformed points correspond to tubes and ellipsoids in the parametric model, and the field function comprises a tube field formula and an ellipsoid field formula, wherein fitting the parametric model comprises applying the tube field formula and the ellipsoid field formula to the tubes and ellipsoids, respectively.

10. The method according to claim 9 , wherein fitting the parametric model further comprises applying a blending operator to the tubes and ellipsoids.

11. The method according to claim 1 , wherein the anatomic features comprise at least one of a tube centerline, tube orientation, tube area, tube ellipse extent, and a ridge point.

12. The method according to claim 1 , wherein relating the electrical data to the fitted parametric model comprises minimizing an objective function that describes an estimated error of the parametric model with respect to the electrical data.

13. The method according to claim 12 , wherein minimizing an objective function comprises imposing constraints from the statistical prior on the objective function.

14. The method according to claim 12 , wherein the objective function comprises a cost function.

15. The method according to claim 12 , wherein minimizing an objective function is performed by assigning respective weights to parameters of the parametric model; and iterating the objective function by varying the respective weights in respective iterations of the objective function according to an optimization schedule.

16. The method according to claim 12 , wherein minimizing an objective function comprises computing derivatives of the objective function with respect to parameters of the parametric model.

17. The method according to claim 1 , wherein fitting the parametric model comprises model component based weighting.

18. The method according to claim 1 , wherein fitting the parametric model comprises curvature weighting.

19. The method according to claim 1 , wherein fitting the parametric model comprises skeleton-based fitting.

20. A method, comprising the steps of:

defining, by a computer processor, a parametric model representing a shape of a portion of a heart;

constructing, by the computer processor, a statistical prior of the shape from a dataset of other instances of the portion, wherein constructing the statistical prior comprises preparing segmented data meshes;

fitting, by the computer processor, the parametric model to the statistical prior by computing anatomic features from the data meshes and computing correlation coefficients among different ones of the anatomic features, wherein computing correlation coefficients comprises using a joint distribution model;

inserting a probe into a living subject, the probe having a mapping electrode;

urging the mapping electrode into contacting relationships with tissue in a plurality of locations in the portion of the heart of the subject;

acquiring electrical data from the respective locations;

relating the electrical data to the fitted parametric model to produce an isosurface of the portion of the heart of the subject; and

reconstructing the shape of the portion of the heart of the subject responsively to relating the electrical data.

21. The method according to claim 20 , wherein constructing the statistical prior comprises a cost function based on a joint probability of a feature.

22. The method according to claim 21 , wherein the construction of the statistical prior comprises determining a feature A based on given information of a feature B.