Magnetic Resonance Arterial Spin Tagging for Noninvasive Pharmacokinetic Analysis of Breast Cancer

Abstract

This project is designed to develop and evaluate arterial spin tagging techniques for the non-invasive measurement of breast tissue perfusion. The long term goal is to show that arterial spin tagging can provide comparable tissue information as that currently obtained using dynamic first-pass contrast enhanced imaging. To date, the research effort has focused on the pulse sequence, protocol and image processing software needed for analysis and visualization of the anatomic, functional, and dynamic breast images. Specific tests and analyses of the sequence have been performed to understand and correct for driven equilibrium effects, and for inversion and excitation slice profile mismatch. Software development includes methods for estimating, at each pixel, the T1, the perfusion (as defined byflA), and standard errors, and from these compute a "suspicion index" that the tissue is abnormal. Algorithms for image registration and image display have been developed to allow direct comparison of high resolution T1, first spin echo T2 and proton density, perfusion, and contrast enhanced images. The research effort provides crucial software for acquisition and definitive review of the images, and automation for detailed and thorough statistical analysis of the very large data sets that are obtained.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1998
Accession Number
ADB248332

Entities

People

  • Michael H. Buonocore

Organizations

  • University of California

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Blood Flow
  • Breast Cancer
  • Cancer
  • Case Studies
  • Computer Programs
  • Data Acquisition
  • Data Analysis
  • Health Services
  • Image Processing
  • Imaging Techniques
  • Magnetic Resonance
  • Magnetic Resonance Imaging
  • Medical Personnel
  • Neoplasms
  • Statistical Analysis

Fields of Study

  • Medicine
  • Physics

Readers

  • Cardiovascular Physiology
  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.