System and method for diagnosis tissue samples using fluorescence and raman techniques

• US 20120078524A1
• Filed: 07/29/2011
• Published: 03/29/2012
• Est. Priority Date: 08/08/2007
• Status: Abandoned Application

• Current Assignee: Chemimage Corporation
• Original Assignee: Chemimage Corporation
• Inventors: Stewart, Shona, Cohen, Jeffrey, Maier, John, Drauch, Amy
• Application Number:
  US13/136,366
• Publication Number:
  US 20120078524A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/417 the bone marrow
• CPC Class Codes: G01J 2003/2826 Multispectral imaging, e.g. filter imaging
• CPC Class Codes: G01J 3/02 Details
• CPC Class Codes: G01J 3/0291 Housings; Spectrometer accessories; Spatial arrangement of elements, e.g. folded path arrangements
• CPC Class Codes: G01J 3/0294 Multi-channel spectroscopy
• CPC Class Codes: G01J 3/10 Arrangements of light sources specially adapted for spectrometry or colorimetry
• CPC Class Codes: G01J 3/2823 Imaging spectrometer
• CPC Class Codes: G01J 3/32 Investigating bands of a spectrum in sequence by a single detector
• CPC Class Codes: G01J 3/44 Raman spectrometry; Scattering spectrometry ; Fluorescence spectrometry
• CPC Class Codes: G01N 2021/656 Raman microprobe
• CPC Class Codes: G01N 21/6458 Fluorescence microscopy fluorescence microscopes per se G02B21/0076 and G02B21/16
• CPC Class Codes: G01N 21/65 Raman scattering
• CPC Class Codes: G01N 21/658 enhancement Raman, e.g. surface plasmons
• CPC Class Codes: G01N 2201/1293 resolving multicomponent spectra
• CPC Class Codes: G01N 2201/1296 using neural networks
• CPC Class Codes: G02B 21/16 adapted for ultraviolet illumination ; Fluorescence microscopes G02B21/0076 takes precedence
• CPC Class Codes: G02B 21/365 Control or image processing arrangements for digital or video microscopes G02B21/361, G02B21/362 take precedence
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/60 : relating to pathologies

Abstract

A system and method for determining a diagnosis of a test biological sample. A system comprising a first illumination source to illuminate a sample, a first detector for generating a fluorescence data set of said sample, a means for determining a region of interest, a second illumination source to illuminate said region of interest, a second detector to generate a Raman data set of said region of interest, and a means for determining a diagnosis of said sample. A method comprising illuminating a sample, generating a fluorescence data set of said sample, and assessing the fluorescence data set to identify a region of interest, illuminating a region of interest, and generating Raman data set. This Raman data set may be assessed to determine a diagnosis of the sample. A diagnosis may include a metabolic state, a clinical outcome, a disease progression, a disease state, and combinations thereof.

Claims

1. A method comprising:

illuminating a test biological sample to thereby generate a first plurality of interacted photons;

detecting said first plurality of interacted photons to thereby generate at least one fluorescence data set representative of said sample;

analyzing said fluorescence data set to thereby determine at least one region of interest of said sample;

illuminating said region of interest to thereby generate a second plurality of interacted photons;

detecting said second plurality of interacted photons to thereby generate at least one Raman data set representative of said region of interest;

analyzing said Raman data set to thereby determine a diagnosis, wherein said diagnosis is selected from the group consisting of;

a metabolic state of said sample, a clinical outcome of said sample, a disease progression of said sample, a disease state of said sample, and combinations thereof.

2. The method of claim 1 wherein said fluorescence data set comprises at least one hyperspectral fluorescence image.

3. The method of claim 1 wherein said fluorescence data set comprises at least one of:

a fluorescence spectrum, a spatially accurate wavelength resolved fluorescence image, and combinations thereof.

4. The method of claim 1 wherein said Raman data set comprises at least one hyperspectral Raman image.

5. The method of claim 1 wherein said Raman data set comprises at least one of:

a Raman spectrum, a spatially accurate wavelength resolved Raman image, and combinations thereof.

6. The method of claim 1 wherein said illuminating of said sample further comprises illuminating said sample using UV light.

7. The method of claim 1 wherein said illuminating of said region of interest further comprises illuminating said region of interest using substantially monochromatic light.

8. The method of claim 1 further comprising providing a reference database comprising a plurality of reference data sets, wherein each said reference data set is associated with at least one of:

a known metabolic state, a known clinical outcome, a known disease progression, a known disease state, and combinations thereof.

9. The method of claim 1 wherein said analyzing of said fluorescence data set further comprises comparing said fluorescence data set to at least one reference data set.

10. The method of claim 1 wherein said analyzing of said Raman data set further comprises comparing said Raman data set to at least one reference data set.

11. The method of claim 1 wherein said analyzing of said fluorescence data set further comprises visually assessing said fluorescence data set to thereby identify said region of interest of said test biological sample, wherein said region of interest exhibits characteristic fluorescence.

12. The method of claim 9 wherein said comparing is achieved using a chemometric technique selected from the group consisting of:

principle component analysis, partial least squares discriminate analysis, cosine correlation analysis, Euclidian distance analysis, k-means clustering, multivariate curve resolution, band t. entropy method, mahalanobis distance, adaptive subspace detector, spectral mixture resolution, and combinations thereof.

13. The method of claim 10 wherein said comparing is achieved using a chemometric technique selected from the group consisting of:

principle component analysis, partial least squares discriminate analysis, cosine correlation analysis, Euclidian distance analysis, k-means clustering, multivariate curve resolution, band t. entropy method, mahalanobis distance, adaptive subspace detector, spectral mixture resolution, and combinations thereof.

14. The method of claim 1 wherein said test biological sample comprises at least one of:

a kidney sample, a prostate sample, a breast sample, a pancreatic sample, a brain sample, a skin sample, an intestinal sample, a colon sample, a liver sample, a cardiac sample, a lung sample, an esophageal sample, a bladder sample, a blood sample, a urethral sample, an ovarian sample, a uterine sample, a testicular sample, a bone sample, a stomach sample, a tracheal sample, a tongue sample, a diaphragm sample, a nerve sample, and combinations thereof.

15. The method of claim 1 further comprising passing at least one of said first plurality of interacted photons and said second plurality of interacted photons through a tunable filter.

16. The method of claim 15 wherein said filter comprises a filter selected from the group consisting of:

a liquid crystal tunable filter, a multi-conjugate tunable filter, an acousto-optical tunable filter, a Lyot liquid crystal tunable filter, an Evans split-element liquid crystal tunable filter, a Solc liquid crystal tunable filter, a ferroelectric liquid crystal tunable filter, a Fabry Perot liquid crystal tunable filter, and combinations thereof.

17. The method of claim 1 further comprising:

generating at least one of said fluorescence data set and said Raman data set at a data generation site;

transmitting at least one of said fluorescence data set and said Raman data set over a data communication network to an analysis center;

analyzing at least one of said fluorescence data set and said Raman data set at said analysis center to thereby determine a diagnosis, wherein said diagnosis is selected from the group consisting of;

a metabolic state of said sample, a clinical outcome of said sample, a disease progression of said sample, a disease state of said sample, and combinations thereof; and

transferring said diagnosis to said data generation site via said data communication network.

18. A storage medium containing machine readable program code, which, when executed by a processor, causes said processor to perform the following:

illuminate a test biological sample to thereby generate a first plurality of interacted photons;

detect said first plurality of interacted photons to thereby generate at least one fluorescence data set representative of said sample;

analyze said fluorescence data set to thereby determine a region of interest of said sample;

illuminate said region of interest to thereby generate a second plurality of interacted photons;

detect said second plurality of interacted photons to thereby generate at least one Raman data set representative of said region of interest; and

analyze said Raman data set to thereby determine at least one of;

a metabolic state of said sample, a clinical outcome of said sample, a disease progression of said sample, a disease state of said sample, and combinations thereof.

19. The storage medium of claim 18 wherein said machine readable program code, when executed by a processor, further causes said processor to pass at least one of said first plurality of interacted photons and said second plurality of interacted photons through a tunable filter.

20. The storage medium of claim 18 wherein said machine readable program code, when executed by a processor to analyze at least one of said fluorescence data set and said Raman data set, further causes said processor to:

compare at least one of said fluorescence data set and said Raman data set to at least one reference data set.

21. A system comprising:

a first illumination source configured so as to illuminate a test biological sample to thereby generate a first plurality of interacted photons;

a first detector configured so as to detect said first plurality of interacted photons and generate at least one fluorescence data set representative of said sample;

a means for analyzing said fluorescence data set to thereby determine a region of interest of said test biological sample; and

a second illumination source configured so as to illuminate a region of interest of said biological sample to thereby generate a second plurality of interacted photons;

a second detector configured so as to detect said second plurality of interacted photons and generate at least one Raman data set representative of said region of interest; and

a means for analyzing said Raman data set to thereby determine a diagnosis selected from the group consisting of;

a metabolic state of said sample, a clinical outcome of said sample, a disease progression of said sample, a disease state of said sample, and combinations thereof.

22. The system of claim 21 further comprising at least one tunable filter configured so as to sequentially filter at least one of said first plurality of interacted photons and said second plurality of interacted photons into a plurality of predetermined wavelength bands.

23. The system of claim 22 wherein said tunable filter is selected from the group consisting of:

a liquid crystal tunable filter, a multi-conjugate tunable filter, an acousto-optical tunable filter, a Lyot liquid crystal tunable filter, an Evans split-element liquid crystal tunable filter, a Solc liquid crystal tunable filter, a ferroelectric liquid crystal tunable filter, a Fabry Perot liquid crystal tunable filter, and combinations thereof.

24. The system of claim 21 wherein said first detector comprises a detector selected from the group consisting of:

a focal plane array detector, a CCD, an ICCD, a CMOS detector, and combinations thereof.

25. The system of claim 21 wherein said second detector comprises a detector selected from the group consisting of:

a focal plane array detector, a CCD, an ICCD, a CMOS detector, and combinations thereof.

26. The system of claim 21 wherein said fluorescence data set comprises a hyperspectral fluorescence image.

27. The system of claim 21 wherein said fluorescence data set comprises at least one of:

a fluorescence spectrum, a spatially accurate wavelength resolved fluorescence image, and combinations thereof.

28. The system of claim 21 wherein said Raman data set comprises a hyperspectral Raman data set.

29. The system of claim 21 wherein said Raman data set comprises at least one of:

a Raman spectrum, a spatially accurate wavelength resolved Raman image, and combinations thereof.