Prior image based three dimensional imaging

1. A non-transitory computer readable medium embodying a program of instructions executable by machine to perform steps for facilitating three-dimensional imaging implemented by a computer system, the steps comprising:

receiving prior three-dimensional (3D) image data of vasculature;

intraoperatively acquiring a sparse set of two-dimensional (2D) projection image data of the vasculature, wherein the sparse set of 2D image data is acquired after the 3D image data is acquired;

performing a deformable registration to align the 3D image data with the 2D image data; and

minimizing a difference between the aligned 3D image data and the 2D image data by performing an iterative reconstruction scheme.

2. A method of three-dimensional imaging implemented by a computer system, comprising:

receiving prior three-dimensional (3D) image data and a sparse set of two-dimensional (2D) projection image data of at least one structure of interest, wherein the sparse set of 2D image data is acquired after the 3D image data is acquired;

performing a deformable registration to align the 3D image data with the 2D image data; and

minimizing a difference between the aligned 3D image data and the 2D image data by performing an iterative reconstruction scheme.

3. The method of claim 2 further comprising acquiring the prior 3D image data by performing intraoperative 3D mask and fill image acquisitions.

4. The method of claim 2 further comprising acquiring the prior 3D image data by retrieving pre-operative vessel-only 3D image data from a data source and performing an intraoperative fill 3D image acquisition.

5. The method of claim 2 further comprising intraoperatively acquiring the sparse set of 2D projection image data.

6. The method of claim 2 wherein the sparse set of 2D projection image data corresponds to two viewing angles.

7. The method of claim 2 further comprises establishing control points based on the 3D image data to drive the deformable registration.

8. The method of claim 7 wherein establishing the control points comprises:

determining a centerline of the at least one structure of interest in the 3D image data; and

sparsifying the centerline to generate the control points.

9. The method of claim 7 wherein performing the deformable registration comprises generating a local deformation field that transforms the prior 3D image data along the control points to align with a 2D projection image selected from the sparse set of 2D projection image data.

10. The method of claim 9 further comprising interpolating the local deformation field using a spline.

11. The method of claim 9 wherein generating the local deformation field comprises:

projecting the control points onto the 2D projection image to generate projected control points;

determine a 2D centerline of the at least one structure of interest in the 2D projection image; and

determining differences between the projected control points and the 2D centerline.

12. The method of claim 11 wherein determining the differences between the projected control points and the 2D centerline comprises calculating an in-plane vector indicative of the direction of the 2D centerline for at least one of the projected control points.

13. The method of claim 12 further comprising interpolating multiple in-plane vectors for multiple projected control points to establish a 3D representation of the local deformation field.

14. The method of claim 9 further comprising calculating a global deformation field based on multiple local deformation fields corresponding to multiple 2D projection images selected from the sparse set of 2D projection image data.

15. The method of claim 2 wherein performing the iterative reconstruction scheme comprises performing simultaneous algebraic reconstruction technique.

16. An imaging system, comprising:

a memory device for storing computer readable program code; and

a processor in communication with the memory device, the processor being operative with the computer readable program code to perform steps including

receiving prior mask image data of at least one structure of interest acquired before a contrast agent is administered into the structure of interest,

receiving scan image data of the structure of interest, wherein the scan image data is acquired after the structure of interest is made opaque by the contrast agent,

reconstructing a three-dimensional (3D) mask image dataset based on the prior mask image data,

reconstructing a 3D fill-run-image dataset based on the scan image data,

rigidly aligning the 3D mask image dataset with the 3D fill-run image dataset, and

subtracting the aligned 3D mask image dataset from the 3D fill-run image dataset to generate a subtracted 3D image dataset.

17. The imaging system of claim 16 wherein the structure of interest comprises a blood vessel.

18. The imaging system of claim 16 wherein the processor is further operative with the computer readable program code to align centers of mass of the 3D fill-run image dataset and the 3D mask image dataset.

19. The imaging system of claim 16 wherein the processor is further operative with the computer readable program code to rigidly align the 3D mask image dataset with the 3D fill-run image dataset by

generating a difference image by subtracting the 3D mask image dataset from the 3D fill-run image dataset, and

minimizing magnitude of the difference image.

20. The imaging system of claim 19 wherein the processor is further operative with the computer readable program code to minimize the magnitude of the difference image by applying optimized rotation and translation matrices to the 3D mask image dataset.