Optimization of Technique Factors for Full-Field Digital Mammography and Comparison of Optimized Techniques to Screen-Film Mammography

Abstract

This final report presents progress achieved during a four year pre-doctoral traineeship project to determine the optimum techniques for a flat-panel Cesium-iodide silicon-diode full-field digital mammography system and to compare those optimized techniques to screen-film mammography at equal breast doses. This project work has analyzed the effect of technique factor selection (target-filtration, kvp, and mAs) on image contrast and low- contrast lesion detection under the conditions of matched average glandular dose to an optimized film-screen mammography system. Results indicate that low-contrast lesion detection was optimized for full-field digital mammography by using a softer x-ray beam for thin breasts and a harder x-ray beam for thick breasts. Under the constraint of matched average glandular dose between digital and screen-film mammography systems, optimum low-contrast lesion detection with full-field digital mammography was superior to that. for screen-film mammography for all but the thinnest breasts. The results of this project have been published in two journal articles with another manuscript in preparation for submission and five abstracts have been published and presented at scientific meetings.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2003
Accession Number
ADA420415

Entities

People

  • Edward Hendrick
  • Eric A. Berns

Organizations

  • Northwestern University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Breast Cancer
  • Computer Programs
  • Contrast
  • Detection
  • Detectors
  • Digital Images
  • Electronic Mail
  • Filtration
  • Imaging Techniques
  • Ionization Chambers
  • Materials
  • Measurement
  • North America
  • Optimization
  • Quality Control
  • X Rays

Fields of Study

  • Medicine
  • Physics

Readers

  • Nuclear and Radiation Engineering.
  • Oncology and Biomarker-Based Cancer Detection.