Method and apparatus for path normalization of light transport in tissue

• US 20030040664A1
• Filed: 08/01/2002
• Published: 02/27/2003
• Est. Priority Date: 01/12/2000
• Status: Abandoned Application

• Current Assignee: Suresh Thennadil, Tamara L. Troy, Thomas B. Blank
• Original Assignee: Suresh Thennadil, Tamara L. Troy, Thomas B. Blank
• Inventors: Blank, Thomas B., Troy, Tamara L., Thennadil, Suresh
• Application Number:
  US10/210,914
• Publication Number:
  US 20030040664A1
• Time in Patent Office: Days
• US Class Current: 600/322
• 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 5/441 Skin evaluation, e.g. for skin disorder diagnosis

Abstract

A method of measuring in vivo skin tissue thickness employs noninvasive NIR absorbance spectra. Constituents of a tissue sample are characterized and quantified based on differing absorbance spectra and scattering properties, allowing thickness and chemical composition of layers to be estimated. Pathlength normalization reduces spectral interference in predicting analyte concentrations.

Claims

1. A non-invasive method of estimating thickness of skin tissue in vivo and characterizing constituents of tissue layers, comprising the steps of:

providing a calibration set of exemplary measurements;

providing a library of normalized NIR absorbance spectra of key indicators;

measuring an NIR absorbance spectrum of a target layer at a tissue sample site;

normalizing said spectrum of said tissue site relative to said spectra of said key indicators;

calculating the magnitude of at least one of said constituents; and

applying a calibration model to said calculated magnitude to characterize said tissue layers.

2. The method of claim 1, wherein said key indicators comprise chemical and structural components that are primary absorbers and scatterers within a particular tissue layer, and wherein said magnitude of said key indicators is greater in said particular layer of said tissue sample than in any other layer of said tissue sample, such that said magnitude of said key indicators is specific to said particular tissue layer, so that said particular tissue layer can be characterized according to said magnitudes of said key indicators.

3. The method of claim 2, wherein tissue layers that can be characterized by calculating said magnitudes of said key indicators include any of:

subcutaneous tissue;

dermis;

epidermis; and

stratum corneum.

4. The method of claim 2, wherein said key indicators are determined from a priori knowledge of the composition and structure of said tissue layers, and wherein structural and chemical components that can serve as key indicators include any of:

trigylcerides;

collagen bundles;

water;

blood;

keratinocytes;

fatty acids;

sterols;

sphingolipids;

pigments;

corneocytes;

keratinized cells; and

sebum.

5. The method of claim 2, wherein said measuring step comprises the steps of:

selecting a target tissue layer;

selecting at least one target key indicator specific to said target tissue layer;

limiting said spectrum to a wavelength region wherein said at least one target key indicator absorbs and scatters, and wherein optimal penetration of transmitted energy to said target layer is possible.

6. The method of claim 2, wherein said normalizing step comprises:

projecting said normalized spectra of said key indicators on said measured spectrum.

7. The method of claim 2, wherein said normalizing step comprises:

providing a basis set, wherein said basis set comprises the spectra of said key indicators.

8. The method of claim 7, wherein said calculation step comprises:

applying a partial least squares regression to calculate said magnitudes.

9. The method of claim 2, wherein said calculated magnitudes of said key indicators provide relative concentrations of said structural and chemical components.

10. The method of claim 9, wherein said calibration step comprises:

applying a calibration model to said relative concentrations to determine an actual concentration in said target layer, wherein said calibration model is calculated from said calibration set of exemplary measurements.

11. The method of claim 9, wherein said calibration step comprises:

applying a calibration model to said relative concentrations to determine thickness of said target layer, wherein said calibration model is calculated from said calibration set of exemplary measurements.

12. The method of claim 11, wherein said exemplary measurements comprise calculated relative concentrations of said chemical and structural components and tissue layer thickness determinations.

13. The method of claim 12, wherein said calibration model is calculated using any of multiple linear regression, partial least squares regression, and artificial neural networks.

14. The method of claim 1, wherein said calibration set comprises NIR spectral measurements of an exemplary sample of skin tissue, tissue layer thickness measurements determined from biopsies of said exemplary sample, and determinations of chemical composition of said layers of said biopsy samples.

15. The method of claim 14, wherein multivariate regression analysis relates said NIR spectral measurements of said exemplary tissue sample to said layer thickness and chemical composition determinations from said biopsy samples.

16. The method of claim 1, wherein said calibration set comprises a tissue model that represents the fundamental absorbing and scattering characteristics of an in vivo tissue system.

17. The method of claim 16, wherein said tissue model employs a simulation method, wherein photon propagation of light through said tissue model is simulated, and wherein said photon propagation simulation yields a simulated diffuse reflectance spectrum comparable to an actual reflectance spectrum.

18. The method of claim 17, wherein said simulation method is a Monte Carlo simulation.

19. The method of claim 1, further comprising the step of;

summing said thickness estimates of individual target layers;

whereby a total thickness of said tissue sample is calculated.

20. A non-invasive method of estimating thickness of in vivo skin tissue comprising the steps of:

providing a calibration set of exemplary measurements;

measuring a NIR absorbance spectrum of a target layer at a tissue sample site;

applying a calibration model to said absorbance spectrum; and

determining a thickness estimate of said target layer of said tissue sample.

21. The method of claim 20, wherein said calibration set comprises spectral measurements of a target tissue site and tissue layer thickness determinations from an exemplary population of subjects.

22. The method of claim 21, wherein multivariate regression analysis relates said exemplary spectral measurements to said exemplary tissue layer thickness determinations.

23. The method of claim 22, wherein said calibration model is calculated from said calibration set using any of multiple linear regression, partial least squares regression, and artificial neural networks.

24. The method of 20, further comprising the step of;

summing said thickness estimates of individual target layers;

whereby a total thickness of said tissue sample is calculated.

26. The method of claim 24, wherein said function is calculated from a plurality of tissue models using Monte Carlo simulations.

25. In a method for noninvasive prediction of blood analytes:

a method of reducing interference in a measured NIR spectrum of a sampled tissue site due to non-linear variation in optical properties of individual layers of said tissue site comprising the steps of;

determining concentrations of key indicators specific to said tissue layers;

determining thickness of said tissue layers;

processing said concentration determinations and said thickness determination through a non-linear function whereby said measured NIR spectrum is normalized.