Non-invasive method of determining skin thickness and characterizing layers of skin tissue in vivo

• US 6,456,870 B1
• Filed: 02/25/2000
• Issued: 09/24/2002
• Est. Priority Date: 07/22/1999
• Status: Expired due to Fees

• Current Assignee: GLT Acquisition Corporation
• Original Assignee: Sensys Medical, Inc. (Sensys Medical Limited)
• Inventors: Ruchti, Timothy L., Kees, Glenn Aaron, Rennert, Jessica
• Primary Examiner(s): WINAKUR, ERIC FRANK
• Application Number:
  US09/514,039
• Time in Patent Office: 942 Days
• Field of Search: 600/310, 600/473, 600/475, 356/402, 250/340, 250/341.1, 250/339.01
• US Class Current: 600/475
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0233 Optical standards
• 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 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G01N 21/274 Calibration, base line adjustment, drift correction
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 21/4785 Standardising light scatter apparatus; Standards therefor
• CPC Class Codes: G01N 21/49 within a body or fluid
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A novel approach to measuring the overall and layer-by-layer thickness of in vivo skin tissue based on near infrared absorbance spectra is described. The different biological and chemical compounds present in the various layers of a tissue sample have differing absorbance spectra and scattering properties that enable them to be discerned and quantified, thus allowing an estimate of the thickness of the tissue being sampled. The method of the invention also yields the chemical composition of the absorbing and/or scattering species of each layer. Additionally, a method of path length normalization for the purpose of noninvasive analyte prediction on the basis of skin thickness and layer constituents is provided.

Claims

1. A non-invasive method of estimating thickness of in vivo skin tissue 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 key indicators; 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, and whereby 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 said 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; and

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 the step of:

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

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

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 the step of:

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 the step of;

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 the step of:

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 whereby 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.

23. The method of claim 11 or the method of claim 19, further comprising the step of;

summing said thickness estimates of individual target layers;

whereby a total thickness of said tissue sample is calculated.

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

providing a calibration set of exemplary measurements;

measuring an 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.

20. The method of claim 19, wherein said calibration set comprises spectral measurements of a target tissue site and tissue layer thickness determinations from an exemplary population of subjects.

21. The method of claim 20, further comprising the step of:

using multivariate regression analysis to relate said exemplary spectral measurements to said exemplary tissue layer thickness determinations.

22. The method of claim 21, 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. 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 according to said concentrations of said key indicators; and

processing said concentration determinations and said thickness determinations through a non-linear function;

wherein said measured NIR spectrum is normalized.

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