Breast Cancer Screening Using Photonic Technology

Abstract

The research carried out during the current reporting period involved: (a) time-sliced and spectroscopic two-dimensional (2-D) near-infrared transillumination imaging of normal and cancerous in vitro human breast tissue specimens; (b) correlating the results of optical measurements with NMR measurements; (c) derivation of analytical solutions of the polarized photon transport equation that provides a more accurate analytical basis for developing three-dimensional (3-D) inverse image reconstruction techniques; and (d) development of forward models and 3-D inverse image reconstruction methods. Images recorded with earlier temporal slices of transmitted light were found to highlight cancerous tissues while those recorded with later slices accentuated normal fibrous tissues. Initial spectroscopic imaging experiments show that the ratio, R of light intensity transmitted through the cancerous tissue to that through the corresponding normal tissue show a wavelength dependent variation that has the potential to be used as a useful parameter for cancer identification. Analytical solutions of the polarized photon transport equation are more complete and enable description of polarized light imaging. Faster and more noise-resistant 3-D image reconstruction schemes are being pursued.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2001
Accession Number
ADA399367

Entities

People

  • Robert Alfano

Organizations

  • City University of New York

Tags

Communities of Interest

  • Air Platforms
  • Biomedical

DTIC Thesaurus Topics

  • Boltzmann Equation
  • Breast Cancer
  • Carcinoma
  • Detection
  • Detectors
  • Distribution Functions
  • Electromagnetic Radiation
  • Equations
  • Geometry
  • Image Reconstruction
  • Light Sources
  • Neoplasms
  • Optical Properties
  • Refractive Index
  • Spatial Distribution
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Image Processing and Computer Vision.
  • Medical Imaging.