Multi-spectral three dimensional imaging system and method

1. A computer-implemented method of creating an image of a specimen, the method comprising:

receiving, by a processor, a first image of a first section of a specimen, the first image created using a first wavelength of invisible light;

receiving, by the processor, a second image of a second section of the specimen, the second section being adjacent to the first section and the second image created using the first wavelength of invisible light;

co-registering, by the processor, the first image and the second image;

creating, by the processor, a single-plane image of the first section using a next-image process;

de-blurring, by the processor, the first image; and

determining at least one tissue type from a visible light image and wherein the de-blurring is performed based on the at least one tissue type,

wherein the specimen is contacted with at least one first fluorophore having an emission spectrum in the range of approximately 200 nm to 1000 nm.

2. The method of claim 1 , further comprising:

receiving, by the processor, an image of an Nth section of the specimen created using the first wavelength of invisible light; and

creating, by the processor, a 3D image of the specimen using at least three images of adjacent specimen sections.

3. The method of claim 2 , wherein the creating of the 3D image is performed via a de-blurring process, wherein the de-blurring process includes one of subsurface fluorescence removal, resolving an aberration in camera optics or physical correction.

4. The method of claim 3 , wherein the specimen is contacted with a first fluorophore, and a second, different fluorophore, said second fluorophore having an emission spectrum in the range of, but not limited to, approximately 200 nm to 1000 nm.

5. The method of claim 1 , further comprising:

receiving, by the processor, a third image of the first section of the specimen, the third image created using a second wavelength of invisible light;

receiving, by the processor, a fourth image of a second section of the specimen, the fourth image created using the second wavelength of invisible light;

co-registering, by the processor, the third image and the fourth image; and

creating, by the processor, a single-plane image of the first section using a next-image process.

6. The method of claim 1 , wherein the de-blurring includes one of an analytical solution, a Monte Carlo simulation, a point spread function method, and a deconvolution method.

7. The method of claim 6 , wherein the deconvolution method includes one of: a measured point spread function kernel, a simulated point spread function kernel, a Richardson-Lucy method, a Weiner filter deconvolution, a Van Cittert deconvolution, a blind deconvolution, and a regularized deconvolution method.

8. The method of claim 1 , further comprising:

receiving, by the processor, an image of the first section of the specimen created using visible light; and

co-registering, by the processor, the images of the first section of the specimen.

9. The method of claim 1 , wherein the step of de-blurring is performed based on a plurality of tissue types.

10. A system for creating an image of a specimen comprising:

a light source;

an image capture device;

a visable light source;

a processor configured to:

receive a first image of a first section of a specimen, the first image created using a first wavelength of invisible light;

receive a second image of a second section of the specimen, the second section being adjacent to the first section and the second image created using the first wavelength of invisible light;

co-register the first image and the second image; and

create a single-plane image of the first section using a next-image process;

receive a first visible light image of the first section of the specimen created using visible light;

co-register the images of the first section of the specimen;

de-blur the first image: and

determine at least one tissue type from a visible light image, wherein the de-blurring is performed based on the at least one tissue type.

11. The system of claim 10 , further comprising:

a filter.

12. The system of claim 10 , wherein the processor is further configured to:

receive an Nth image of an Nth section of the specimen created using the first wavelength of invisible light; and

create a 3D image of the specimen using at least three images of adjacent specimen sections.

13. The system of claim 12 , wherein the creating of the 3D image is performed via a next-image process.

14. The system of claim 10 , wherein the specimen is contacted with a first fluorophore and a second, different fluorophore, said second fluorophore having an emission spectrum in the range of approximately 200 nm to 1000 nm.

15. The system of claim 10 , wherein the processor is further configured to:

receive a third image of the first section of the specimen, the third image created using a second wavelength of invisible light;

receive a fourth image of a second section of the specimen, the fourth image created using the second wavelength of invisible light;

co-register the third image and the fourth image; and

create a single-plane image of the first section using a next-image process.

16. The system of claim 10 , wherein the de-blurring includes one of an analytical solution to optical transport, a Monte Carlo simulation, a point spread function method, and a deconvolution method.

17. The system of claim 16 , wherein the deconvolution method includes one of a measured point spread function kernel, a simulated point spread function kernel, a Richardson-Lucy method, a Weiner filter deconvolution, a Van Cittert deconvolution, a blind deconvolution, and a regularized deconvolution method.

18. The system of claim 10 , wherein the specimen is contacted with at least one first fluorophore having an emission spectrum in the range of approximately 200 nm to 1000 nm.

19. The system of claim 10 , further comprising:

a display.

20. The system of claim 10 , wherein the first image is de-blurred based on a plurality of tissue types.