Texture-based multi-dimensional medical image registration

• US 20080143707A1
• Filed: 11/28/2007
• Published: 06/19/2008
• Est. Priority Date: 11/28/2006
• Status: Abandoned Application

• Current Assignee: Calgary Scientific Inc.
• Original Assignee: Calgary Scientific Inc.
• Inventors: Mitchell, Ross
• Application Number:
  US11/987,187
• Publication Number:
  US 20080143707A1
• Time in Patent Office: Days
• US Class Current: 345/418
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2200/28 involving image processing hardware
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/32 using correlation-based methods

Abstract

The present invention relates to a method for registering multi-dimensional image data. First and second multi-dimensional texture datasets are determined in dependence upon received first and second multi-dimensional image datasets, respectively. Initial transform data are then provided. The second multi-dimensional texture dataset is then transformed and interpolated using the transform data. Difference metric data are determined in dependence upon the first multi-dimensional texture dataset and the transformed second multi-dimensional texture dataset. Using an improvement process the transform data are adjusted. The transformation and adjustment of the transform data is iterated until a stopping criterion is satisfied. Use of texture data allows employment of a texture unit of a graphics processor for the transformation, the interpolation and the determination of the difference metric data.

Claims

1. A method for registering multi-dimensional image data comprising:

a) providing a first and a second multi-dimensional image dataset, the first and the second multi-dimensional image dataset being indicative of an image of one of an object and a physical event;

b) determining a first and a second multi-dimensional texture dataset in dependence upon the first and the second multi-dimensional image dataset, respectively;

c) providing transform data;

d) transforming and interpolating the second multi-dimensional texture dataset using the transform data;

e) determining difference metric data in dependence upon the first multi-dimensional texture dataset and the transformed second multi-dimensional texture dataset;

f) using an improvement process adjusting the transform data in dependence upon the difference metric data; and

,

g) iterating d) to f) until a stopping criterion is satisfied.

2. A method for registering multi-dimensional image data as defined in claim 1 comprising providing at least one of the transformed second multi-dimensional texture dataset and the transform data.

3. A method for registering multi-dimensional image data as defined in claim 2 wherein at least d) and e) are performed using a graphics processor.

4. A method for registering multi-dimensional image data as defined in claim 2 wherein at least d) and e) are performed using a texture unit of one of a Graphics Processing Unit (GPU) and General Purpose Graphics Processing Unit (GPGPU).

5. A method for registering multi-dimensional image data as defined in claim 4 comprising storing the first and the second multi-dimensional texture dataset in Video Random Access Memory (VRAM) of the one of a GPU and GPGPU.

6. A method for registering multi-dimensional image data as defined in claim 2 wherein c) the provided transform data are indicative of an identity transform.

7. A method for registering multi-dimensional image data as defined in claim 2 comprising:

determining a normalized gradient field of each of the first and the second multi-dimensional image dataset; and

,

determining the transform data in dependence thereupon.

8. A method for registering multi-dimensional image data as defined in claim 2 comprising:

generating a nonlinear vector deformation field texture dataset using transform data indicative of an identity transform; and

,

adjusting the nonlinear vector deformation field texture dataset using the adjusted transform data.

9. A method for registering multi-dimensional image data as defined in claim 2 wherein d) the second multi-dimensional texture dataset is transformed in dependence upon a linear transform.

10. A method for registering multi-dimensional image data as defined in claim 9 wherein the linear transform is one of a rigid transform and a scaling transform.

11. A method for registering multi-dimensional image data as defined in claim 2 wherein d) the second multi-dimensional texture dataset is transformed in dependence upon a nonlinear transform.

12. A method for registering multi-dimensional image data as defined in claim 8 wherein d) the second multi-dimensional texture dataset is transformed in dependence upon a nonlinear transform using the nonlinear vector deformation field texture dataset.

13. A method for registering multi-dimensional image data as defined in claim 2 wherein d) the second multi-dimensional texture dataset is interpolated after being transformed.

14. A method for registering multi-dimensional image data as defined in claim 12 wherein d) the nonlinear vector deformation field texture dataset is interpolated during the nonlinear transformation of the second multi-dimensional texture dataset.

15. A method for registering multi-dimensional image data as defined in claim 2 wherein e) the difference metric data are determined as a sum of squared differences in dependence upon intensity values of the first multi-dimensional texture dataset and the transformed second multi-dimensional texture dataset.

16. A method for registering multi-dimensional image data as defined in claim 2 wherein e) the difference metric data are determined as a sum of absolute differences in dependence upon intensity values of the first multi-dimensional texture dataset and the transformed second multi-dimensional texture dataset.

17. A method for registering multi-dimensional image data as defined in claim 7 wherein e) the difference metric data are determined as normalized gradient field metric data.

18. A method for registering multi-dimensional image data as defined in claim 2 wherein f) a Nelder-Mead optimization process is used.

19. A method for registering multi-dimensional image data as defined in claim 2 wherein f) a Powell'"'"'s optimization process is used.

20. A method for registering multi-dimensional image data as defined in claim 2 wherein f) a multi-directional search optimization process is used.

21. A method for registering multi-dimensional image data as defined in claim 2 wherein the stopping criterion comprises one of:

a stopping signal received from a user;

a predetermined maximum number of iterations;

a predetermined threshold of the difference metric data; and

,

a predetermined threshold for use in the improvement process.

22. A method for registering multi-dimensional image data as defined in claim 2 wherein b) the first and the second multi-dimensional texture dataset are determined in conformity with a Read Green Blue Alpha (RGBA) format.

23. A method for registering multi-dimensional image data as defined in claim 2 wherein a) the first and the second multi-dimensional image dataset are provided with the first and the second multi-dimensional image dataset being indicative of the images captured using a same image capturing process.

24. A method for registering multi-dimensional image data as defined in claim 2 wherein a) the first and the second multi-dimensional image dataset are provided with the first and the second multi-dimensional image dataset being indicative of the images captured using different image capturing processes.

25. A method for registering multi-dimensional image data as defined in claim 2 wherein c) comprises:

displaying the first and the second multi-dimensional texture dataset;

receiving from a user alignment data; and

,

determining the transform data in dependence upon the alignment data.

26. A method for registering multi-dimensional image data comprising:

a) providing a first and a second multi-dimensional image dataset, the first and the second multi-dimensional image dataset being indicative of an image of one of an object and a physical event;

b) determining a first and a second multi-dimensional texture dataset in dependence upon the first and the second multi-dimensional image dataset, respectively;

c) generating a nonlinear vector deformation field texture dataset using transform data indicative of an identity transform;

d) using a texture unit of one of a Graphics Processing Unit (GPU) and General Purpose Graphics Processing Unit (GPGPU) transforming the second multi-dimensional texture dataset by indexing the nonlinear vector deformation field texture dataset producing a deformation index and using the deformation index indexing the second multi-dimensional texture dataset;

e) using the texture unit of the one of a GPU and GPGPU determining difference metric data in dependence upon the first multi-dimensional texture dataset and the transformed second multi-dimensional texture dataset;

f) using an improvement process adjusting the transform data in dependence upon the difference metric data;

g) using the texture unit of the one of a GPU and GPGPU adjusting the nonlinear vector deformation field texture dataset using the adjusted transform data;

h) iterating d) to g) until a stopping criterion is satisfied; and

,

i) providing at least one of the transformed second multi-dimensional texture dataset and the transform data.

27. A method for registering multi-dimensional image data as defined in claim 26 wherein d) the nonlinear vector deformation field texture dataset is interpolated during the nonlinear transformation of the second multi-dimensional texture dataset.

28. A method for registering multi-dimensional image data as defined in claim 26 comprising storing the first and the second multi-dimensional texture dataset and the nonlinear vector deformation field texture dataset in Video Random Access Memory (VRAM) of the one of a GPU and a GPGPU.

29. A method for registering multi-dimensional image data comprising:

a) providing a first and a second multi-dimensional image dataset, the first and the second multi-dimensional image dataset being indicative of an image of one of an object and a physical event;

b) determining a first and a second multi-dimensional texture dataset in dependence upon the first and the second multi-dimensional image dataset, respectively;

c) determining a normalized gradient field of each of the first and the second multi-dimensional image dataset and determining transform data in dependence thereupon;

d) using a texture unit of one of a Graphics Processing Unit (GPU) and General Purpose Graphics Processing Unit (GPGPU)transforming and interpolating the second multi-dimensional texture dataset using the transform data;

e) using the texture unit of the one of a GPU and GPGPU determining normalized gradient field metric data based on a calculation of an inner product between corresponding gradient vectors of normalized gradients of the first and the second multi-dimensional texture dataset;

f) using an improvement process adjusting the transform data in dependence upon the difference metric data;

g) iterating d) to f) until a stopping criterion is satisfied; and

,

h) providing at least one of the transformed second multi-dimensional texture dataset and the transform data.

30. A method for registering multi-dimensional image data as defined in claim 29 comprising storing the first and the second multi-dimensional texture dataset in Video Random Access Memory (VRAM) of the one of a GPU and GPGPU.

31. A storage medium having stored therein executable commands for execution on at least a processor, the at least a processor when executing the commands performing:

a) receiving a first and a second multi-dimensional image dataset, the first and the second multi-dimensional image dataset being indicative of an image of one of an object and a physical event;

b) determining a first and a second multi-dimensional texture dataset in dependence upon the first and the second multi-dimensional image dataset, respectively;

c) receiving transform data;

d) transforming and interpolating the second multi-dimensional texture dataset using the transform data;

e) determining difference metric data in dependence upon the first multi-dimensional texture dataset and the transformed second multi-dimensional texture dataset;

f) using an improvement process adjusting the transform data in dependence upon the difference metric data; and

,

g) iterating d) to f) until a stopping criterion is satisfied.

32. A storage medium having stored therein executable commands for execution on at least a processor, the at least a processor when executing the commands performing:

a) receiving a first and a second multi-dimensional image dataset, the first and the second multi-dimensional image dataset being indicative of an image of one of an object and a physical event;

b) determining a first and a second multi-dimensional texture dataset in dependence upon the first and the second multi-dimensional image dataset, respectively;

c) generating a nonlinear vector deformation field texture dataset using transform data indicative of an identity transform;

d) using a texture unit of one of a Graphics Processing Unit (GPU) and General Purpose Graphics Processing Unit (GPGPU) transforming the second multi-dimensional texture dataset by indexing the nonlinear vector deformation field texture dataset producing a deformation index and using the deformation index indexing the second multi-dimensional texture dataset;

e) using the texture unit of the one of a GPU and GPGPU determining difference metric data in dependence upon the first multi-dimensional texture dataset and the transformed second multi-dimensional texture dataset;

f) using an improvement process adjusting the transform data in dependence upon the difference metric data;

g) using the texture unit of the one of a GPU and GPGPU adjusting the nonlinear vector deformation field texture dataset using the adjusted transform data;

h) iterating d) to g) until a stopping criterion is satisfied; and

,

i) providing at least one of the transformed second multi-dimensional texture dataset and the transform data.

33. A storage medium having stored therein executable commands for execution on at least a processor, the at least a processor when executing the commands performing:

a) receiving a first and a second multi-dimensional image dataset, the first and the second multi-dimensional image dataset being indicative of an image of one of an object and a physical event;

b) determining a first and a second multi-dimensional texture dataset in dependence upon the first and the second multi-dimensional image dataset, respectively;

c) determining a normalized gradient field of each of the first and the second multi-dimensional image dataset and determining transform data in dependence thereupon;

d) using a texture unit of one of a Graphics Processing Unit (GPU) and General Purpose Graphics Processing Unit (GPGPU) transforming and interpolating the second multi-dimensional texture dataset using the transform data;

e) using the texture unit of the one of a GPU and GPGPU determining normalized gradient field metric data based on a calculation of an inner product between corresponding gradient vectors of normalized gradients of the first and the second multi-dimensional texture dataset;

f) using an improvement process adjusting the transform data in dependence upon the difference metric data;

g) iterating d) to f) until a stopping criterion is satisfied; and

,

h) providing at least one of the transformed second multi-dimensional texture dataset and the transform data.

34. A system for registering multi-dimensional image data comprising:

an input port for receiving a first and a second multi-dimensional image dataset, the first and the second multi-dimensional image dataset being indicative of an image of one of an object and a physical event;

circuitry connected to the input port, the circuitry for;

a) determining a first and a second multi-dimensional texture dataset in dependence upon the first and the second multi-dimensional image dataset, respectively;

b) providing transform data;

c) transforming and interpolating the second multi-dimensional texture dataset using the transform data;

d) determining difference metric data in dependence upon the first multi-dimensional texture dataset and the transformed second multi-dimensional texture dataset;

e) using an improvement process adjusting the transform data in dependence upon the difference metric data; and

,

f) iterating c) to e) until a stopping criterion is satisfied; and

,

an output port connected to the circuitry, the output port for providing at least one of the transformed second multi-dimensional texture dataset and the transform data.

35. A system for registering multi-dimensional image data as defined in claim 34 wherein the circuitry comprises a graphics processor for performing at least c) and d).

36. A system for registering multi-dimensional image data as defined in claim 35 wherein the graphics processor is one of a Graphics Processing Unit (GPU) and General Purpose Graphics Processing Unit (GPGPU).

37. A system for registering multi-dimensional image data as defined in claim 35 wherein the circuitry comprises a Central Processing Unit (CPU).