Non-Invasive NIR Sensor for Quantification of Deep Tissue Oxygenation. Phase 1.

Abstract

A breadboard NIR sensor system was developed for the purpose of accurate quantification f deep tissue oxygenation based on a novel Pseudo-Random Modulation/Resolution Enhancement Technique (PRM/RET). It consists of a CW modulated diode laser coupled with optical fiber, a detector, and a sampling oscilloscope. Temporal resolution of 40 psec with a 200 MHZ PRM system is demonstrated, which is 100 fold improvement over the conventional limit of the PRM technique. Experiments using a homogeneous phantom demonstrate a measurement accuracy of 0.001 cm' for absorption coefficient and .07cm-' for scattering coefficient. This accuracy provides the detectability of a few percent oxygenation change in tissue blood. The differential measurement using two MR wavelengths demonstrates the utility or absolute qunatification of oxygenation. A simple method was developed to perform sensitive and self-consistent absorption and scattering coefficients measurement, suitable for battle field applications. A remarkable depth resolution was demonstrated in an absorber depth measurement experiment, suggesting the direct applicability of the sensor system to clinical deep tissue diagnosis. Design for Phase II prototype system which is portable, low cost, and low power consumption is presented.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1995
Accession Number
ADB204824

Entities

People

  • Hwang Sheng Lee

Tags

Communities of Interest

  • Advanced Electronics
  • Biomedical
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Blood
  • Blood Flow
  • Cardiovascular Physiological Phenomena
  • Data Analysis
  • Detection
  • Detectors
  • Field Effect Transistors
  • Health Services
  • Hemorrhagic Shock
  • Laser Diodes
  • Measurement
  • Medical Personnel
  • Optical Properties
  • Statistical Algorithms
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Oncology and Biomarker-Based Cancer Detection.
  • Optical Physics and Photonics.

Technology Areas

  • Directed Energy
  • Directed Energy - Lasers