Atlas-assisted synthetic computed tomography using deformable image registration

• US 8,774,481 B2
• Filed: 03/25/2011
• Issued: 07/08/2014
• Est. Priority Date: 03/25/2010
• Status: Active Grant

• Current Assignee: Emory University
• Original Assignee: Emory University
• Inventors: Schreibmann, Eduard, Fox, Tim
• Primary Examiner(s): Seth, Manav
• Application Number:
  US13/072,144
• Publication Number:
  US 20110235884A1
• Time in Patent Office: 1,201 Days
• Field of Search: None
• US Class Current: 382/131
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10104 Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 7/30 Determination of transform parameters for the alignment of images, i.e. image registration

Abstract

Disclosed are systems for and methods of creating a synthetic image by registering a reference or atlas image to a clinical image using both rigid and deformable image registration algorithms. In some embodiments, a synthetic computed tomography (CT) image may be created by registering an atlas CT image to a clinical image such as an MR scan. Rigid registration in some embodiments may be followed by a smoothing B-spline transform algorithm with a mutual information similarity metric and an optimizer; followed then by an image signal intensity algorithm with displacement vectors at each voxel and diffeomorphic transformations.

Claims

1. A computing system for creating a synthetic computed-tomography (CT) image volume, comprising:

processing circuitry comprising a processor and memory;

a rigid registration algorithm, which when implemented by the processing circuitry, is configured to register a reference CT image volume to a first clinical image volume and thereby produce a first registration;

a smoothing function, which when implemented by the processing circuitry, is configured to further refine said first registration and thereby produce a smoothed registration;

a voxelwise intensity algorithm, which when implemented by the processing circuitry, is configured to further refine said smoothed registration and thereby produce said synthetic CT image volume having characteristics correlated to those of said first clinical image volume;

an attenuation coefficient map generating algorithm, which when implemented by the processing circuitry, is configured to derive a map of attenuation coefficients based on said synthetic CT image volume; and

an attenuation correction engine, which when implemented by the processing circuitry, is configured to correct attenuation in a second clinical image volume based on said map of attenuation coefficients.

3. The computing system of claim 1, wherein said rigid registration algorithm comprises:

an affine transform algorithm having a plurality of variables including translation, rotation, scale, and shear;

a similarity metric representing alignment of said reference CT image volume to said first clinical image volume;

an optimizer configured to iteratively adjust said plurality of variables until said similarity metric achieves a minimum limit.

4. The computing system of claim 3, wherein said optimizer further comprises:

an iteration maximum;

a similarity metric minimum; and

a module configured to halt optimizing after either said iteration maximum equals about thirty or said similarity metric minimum falls below 10<sup>−

5</sup>.

5. The computing system of claim 1, wherein said smoothing function comprises:

a B-spline transform wherein a deformation is defined by a grid of nodes superimposed on said first registration, said B-spline transform having a plurality of variables including node deformation;

a similarity metric representing alignment of said first registration to said first clinical image volume; and

an optimizer configured to iteratively adjust said plurality of variables until said similarity metric achieves a minimum limit.

6. The computing system of claim 5, wherein said B-spline transform comprises:

a first stage B-spline transform wherein said grid of nodes comprises a first grid of nodes arrayed at a first spacing distance superimposed on said first registration, said first stage B-spline transform producing an intermediate registration; and

a second stage B-spline transform wherein said grid of nodes further comprises a second grid of nodes arrayed at a second spacing distance superimposed on said intermediate registration, wherein said second spacing distance is shorter than said first spacing distance.

7. The computing system of claim 1, wherein said voxelwise intensity algorithm comprises:

a deformable image registration transform having a plurality of nonlinear variables;

a displacement vector defined at least at each voxel;

a similarity metric representing alignment of said smoothed registration to said first clinical image volume; and

an optimizer configured to iteratively adjust said plurality of nonlinear variables according to said displacement vector until said similarity metric achieves a minimum limit.

8. The computing system of claim 7, wherein said optimizer is further configured to iteratively adjust said plurality of nonlinear variables according to a partial differential equation having a diffeomorphic update step.

9. The computing system of claim 7, wherein said similarity metric comprises a variational expression of a mutual information component that is varied according to a local value of an image intensity gradient based at least partially on said displacement vector.

23. The system of claim 1, further comprising a selection tool configured to select said reference CT image volume from among a plurality of candidate CT image volumes based on a comparison of a desired similarity metric to candidate similarity metrics corresponding to the plurality of candidate CT image volumes.

24. The system of claim 1, wherein the attenuation correction engine is further configured to derive said map of attenuation coefficients.

2. A computing system for creating a synthetic computed-tomography (CT) image volume, comprising:

processing circuitry comprising a processor and memory;

a rigid registration algorithm, which when implemented by the processing circuitry, is configured to register a reference CT image volume to a first clinical image volume and thereby produce a first registration, wherein said rigid registration algorithm is further configured to register said first clinical image volume to each of a plurality of candidate CT image volumes and thereby obtain a plurality of corresponding candidate similarity metrics;

a smoothing function, which when implemented by the processing circuitry, is configured to further refine said first registration and thereby produce a smoothed registration;

a voxelwise intensity algorithm, which when implemented by the processing circuitry, is configured to further refine said smoothed registration and thereby produce said synthetic CT image volume having characteristics correlated to those of said first clinical image volume; and

a selection tool, which when implemented by the processing circuitry, is configured to select said reference CT image volume from among said plurality of candidate CT image volumes based on a comparison of each of said plurality of corresponding candidate similarity metrics to a desired similarity metric.

25. The system of claim 2, further comprising:

a map of attenuation coefficients based on said synthetic CT image volume; and

an attenuation correction engine configured to correct attenuation in a second clinical image volume based on said map of attenuation coefficients.

10. A method of creating a synthetic computed-tomography (CT) image volume, comprising:

obtaining a first clinical image volume;

retrieving a reference CT image volume;

applying a rigid registration algorithm to register said reference CT image volume to said first clinical image volume and thereby produce a first registration;

applying a smoothing function to said first registration and thereby produce a smoothed registration;

applying a voxelwise intensity algorithm to said smoothed registration and thereby produce said synthetic CT image volume having characteristics correlated to those of said first clinical image volume;

obtaining a second clinical image volume;

deriving a map of attenuation coefficients based on said synthetic CT image volume; and

correcting said second clinical image volume for attenuation based on said map of attenuation coefficients.

11. The method of claim 10, wherein said step of retrieving said reference CT image volume comprises:

establishing a desired similarity metric;

selecting a plurality of candidate CT image volumes;

executing said rigid registration algorithm to register said first clinical image volume to each of said plurality of candidate CT image volumes and thereby obtain a plurality of corresponding candidate similarity metrics; and

selecting said reference CT image volume from among said plurality of candidate CT image volumes based on a comparison of each of said plurality of corresponding candidate similarity metrics to a desired similarity metric.

12. The method of claim 10, wherein said step of applying said rigid registration algorithm comprises:

executing an affine transform algorithm having a plurality of variables including translation, rotation, scale, and shear;

selecting a similarity metric representing alignment of said reference CT image volume to said first clinical image volume; and

optimizing said variables iteratively until said similarity metric achieves a minimum limit.

13. The method of claim 12, wherein said step of optimizing further comprises:

setting an iteration maximum;

setting a similarity metric minimum; and

halting optimizing after either said iteration maximum equals about thirty or said similarity metric minimum falls below 10<sup>−

5</sup>.

14. The method of claim 10, wherein said step of applying said smoothing function comprises:

executing a B-spline transform wherein a deformation is defined by a grid of nodes superimposed on said first registration, said B-spline transform having a plurality of variables including node deformation;

selecting a similarity metric representing alignment of said first registration to said first clinical image volume; and

optimizing said plurality of variables iteratively until said similarity metric achieves a minimum limit.

15. The method of claim 14, wherein said step of executing said B-spline transform further comprises:

executing a first stage B-spline transform wherein said grid of nodes comprises a first grid of nodes arrayed at a first spacing distance superimposed on said first registration, said first stage B-spline transform producing an intermediate registration; and

executing a second stage B-spline transform wherein said grid of nodes further comprises a second grid of nodes arrayed at a second spacing distance superimposed on said intermediate registration, wherein said second spacing distance is shorter than said first spacing distance.

16. The method of claim 10, wherein said step of executing said voxelwise intensity algorithm comprises:

executing a deformable image registration transform having a plurality of nonlinear variables;

defining at least a displacement vector at each voxel;

selecting a similarity metric representing alignment of said smoothed registration to said first clinical image volume; and

optimizing said plurality of nonlinear variables iteratively, according to said displacement vector, until said similarity metric achieves a minimum limit.

17. The method of claim 16, wherein said step of optimizing further comprises:

optimizing said plurality of nonlinear variables according to a partial differential equation having a diffeomorphic update step.

18. The method of claim 16, wherein said step of selecting further comprises:

selecting a similarity metric having a variational expression of a mutual information component that is varied according to a local value of an image intensity gradient based at least partially on said displacement vector.

19. A non-transitory computer-readable medium, contents of which cause a computing system to perform a method of creating a synthetic computed-tomography (CT) image volume, said method comprising:

obtaining a first clinical image volume;

retrieving a reference CT image volume;

applying a rigid registration algorithm to register said reference CT image volume to said first clinical image volume and thereby produce a first registration;

applying a smoothing function to said first registration and thereby produce a smoothed registration;

applying a voxelwise intensity algorithm to said smoothed registration and thereby produce said synthetic CT image volume having characteristics correlated to those of said first clinical image volume;

obtaining a second clinical image volume;

deriving a map of attenuation coefficients based on said synthetic CT image volume; and

correcting said second clinical image volume for attenuation based on said map of attenuation coefficients.

20. The non-transitory computer-readable medium of claim 19, wherein said step of applying said rigid registration algorithm comprises:

executing an affine transform algorithm having a plurality of variables including translation, rotation, scale, and shear;

selecting a similarity metric representing alignment of said reference CT image volume to said first clinical image volume; and

optimizing said plurality of variables iteratively until said similarity metric achieves a minimum limit.

21. The non-transitory computer-readable medium of claim 19, wherein said step of applying said smoothing function comprises:

executing a B-spline transform wherein a deformation is defined by a grid of nodes superimposed on said first registration, said B-spline transform having a plurality of variables including node deformation;

selecting a similarity metric representing alignment of said first registration to said first clinical image volume; and

optimizing said plurality of variables iteratively until said similarity metric achieves a minimum limit.

22. The non-transitory computer-readable medium of claim 19, wherein said step of executing said voxelwise algorithm comprises:

executing a deformable image registration transform having a plurality of nonlinear variables;

defining at least a displacement vector at each voxel;

selecting a similarity metric representing alignment of said smoothed registration to said first clinical image volume; and

optimizing said plurality of nonlinear variables iteratively, according to said displacement vector, until said similarity metric achieves a minimum limit.