Method of determining a condition of a tissue

• US 8,005,527 B2
• Filed: 12/12/2007
• Issued: 08/23/2011
• Est. Priority Date: 07/10/2002
• Status: Expired due to Fees

• Current Assignee: Luma Imaging Corp. (SpectraScience, Inc.)
• Original Assignee: Luma Imaging Corp. (SpectraScience, Inc.)
• Inventors: Zelenchuk, Alex R.
• Primary Examiner(s): Smith; Ruth S
• Application Number:
  US11/955,165
• Publication Number:
  US 20080091110A1
• Time in Patent Office: 1,350 Days
• Field of Search: 600/407, 600/473, 600475-478, 436/63, 436/64, 436/172
• US Class Current: 600/407
• CPC Class Codes: A61B 5/0071 by measuring fluorescence emission
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/0084 for introduction into the body, e.g. by catheters A61B5/1459 takes precedence
• CPC Class Codes: A61B 5/0088 for oral or dental tissue
• CPC Class Codes: A61B 5/4255 Intestines, colon or appendix
• CPC Class Codes: A61B 5/4331 of the cervix A61B5/435 takes precedence
• CPC Class Codes: A61B 5/7267 involving training the classification device

Abstract

A system and method for the in situ discrimination of healthy and diseased tissue. A fiberoptic based probe is employed to direct ultraviolet illumination onto a tissue specimen and to collect the fluorescent response radiation. The response radiation is observed at three selected wavelengths, one of which corresponds to an isosbestic point. In one example, the isosbestic point occurs at about 431 nm. The intensities of the observed signals are normalized using the 431 nm intensity. A score is determined using the ratios in a discriminant analysis. The tissue under examination is resected or not, based on the diagnosis of disease or health, according to the outcome of the discriminant analysis.

Claims

1. A method for determining a condition of a tissue, the method comprising:

illuminating a tissue with light at an excitation wavelength;

detecting a first and second intensity of received electromagnetic radiation from the tissue upon illumination at said excitation wavelength, wherein the first intensity corresponds to a first wavelength and the second intensity corresponds to a second wavelength different from the first wavelength;

normalizing the first and second intensities with an intensity corresponding to an isosbestic point; and

determining a condition of the tissue based at least in part on the normalized first and second intensities.

2. The method of claim 1 wherein the excitation wavelength is 337 nm.

3. The method of claim 1 wherein illuminating the tissue comprises illuminating the tissue using an optical fiber.

4. The method of claim 1 further comprising receiving the electromagnetic radiation at a detector from the tissue by way of a plurality of optical fibers.

5. The method of claim 1 wherein the received electromagnetic radiation is fluorescent light.

6. The method of claim 1 wherein normalizing the first and second intensities with an intensity corresponding to the isosbestic point and determining the condition of the tissue based at least in part of on the normalized first and second intensities is conducted, at least in part, using a Bayesian Mahalanobis-based classifier.

7. The method of claim 6 wherein the Bayesian Mahalanobis-based classifier is selected from the group consisting of linear discriminant analysis, quadratic discriminant analysis, and regularized discriminant analysis.

8. The method of claim 1 wherein normalizing the first and second intensities with an intensity corresponding to the isosbestic point and determining the condition of the tissue based at least in part of on the normalized first and second intensities is conducted, at least in part, using a binary tree classifier function.

9. The method of claim 1 wherein normalizing the first and second intensities with an intensity corresponding to the isosbestic point and determining the condition of the tissue based at least in part of on the normalized first and second intensities is conducted, at least in part, using an unsupervised learning cluster classifier.

10. The method of claim 9 wherein the unsupervised learning cluster classifier is selected from the group consisting of hierarchical clustering analysis, principal component analysis, fuzzy c-means analysis, and fuzzy k-means analysis.

11. The method of claim 1 wherein the isosbestic point is a wavelength in a band at 431 nm with a bandwidth under 5 nm.

12. The method of claim 1 wherein normalizing the first and second intensities with an intensity corresponding to an isosbestic point produces the normalized first intensity and the normalized second intensity.

13. The method of claim 12 wherein determining the condition of the tissue based at least in part on the normalized first and second intensities comprises using a classifier function with the normalized first and normalized second intensities as inputs.

14. The method of claim 1 wherein the tissue comprises epithelial cells.

15. The method of claim 1 wherein the tissue is selected from the group consisting of cervical tissue, colonic tissue, gastroesophageal tissue, bladder tissue, and bronchial tissue.

16. A method for determining a condition of a tissue, the method comprising:

illuminating a tissue with light at an excitation wavelength;

detecting a first and second intensity of received electromagnetic radiation from the tissue upon illumination at said excitation wavelength, wherein the first intensity corresponds to a first wavelength and the second intensity corresponds to a second wavelength different from the first wavelength;

normalizing the first and second intensities with an intensity corresponding to an isosbestic point to produce a normalized first intensity and a normalized second intensity, wherein the isosbestic point is a wavelength in a band at 431 nm with a bandwidth under 5 nm; and

determining a condition of the tissue based at least in part on the normalized first and second intensities.