Improved Mammographic Technique for Breast Cancer Diagnosis

Abstract

The goal of the proposed research is to improve the sensitivity of cancer detection in mixed or dense breasts through optimization of mammographic techniques. We propose to develop a novel exposure equalization system that preferentially reduces the incident x ray intensity in the peripheral region of the breast, thereby alleviating the problem of limited latitude of x ray detectors. Optimal imaging technique can then be developed for improving image quality throughout the entire breast. We have performed the following studies in the third year of the funding period: (1) completed the study of breast shape classification, (2) conducted a simulation study to evaluate the effects of x ray equalization, (3) completed the Monte Carlo modeling of a mammographic imaging system with a focused antiscatter grid, (4) constructed prototype filters and evaluated filter alignment by imaging breast phantoms, (5) conceived an improved method for implementation of the x-ray equalization technique for mammography, (6) conducted preliminary studies to demonstrate the feasibility of the new approach, and (7) developed design specifications for the compressible tank component of the patient specific and tissue equivalent x-ray equalization system. The development of the new method is a significant step towards the practical implementation of the x-ray equalization technique.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1997
Accession Number
ADA334024

Entities

People

  • Heang-Ping Chan

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Boundaries
  • Breast Cancer
  • Classification
  • Detection
  • Detectors
  • Filtration
  • Grids
  • Imaging Techniques
  • Intensity
  • Mammography
  • Materials
  • Monte Carlo Method
  • Radiation
  • Simulations
  • Three Dimensional
  • X Rays
  • X-Ray Detectors

Fields of Study

  • Medicine
  • Physics

Readers

  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.
  • Oncology and Biomarker-Based Cancer Detection.