Sodium MRI of Rat Breast Tumors.

Abstract

Our objective is to use sodium MR imaging techniques to differentiate various breast tumor types in the rat. Specifically, we propose to determine and compare the sodium concentration and the Ti and T2 relaxation parameters for two rat breast tumor lines. Since sodium images have intrinsically low signal-to-noise ratios (SNR), the first part of this research involves implementing various methods to increase the SNR of sodium images. Three methods were proposed: use a higher field MR magnet, build an RF coil pair that transmits with a body coil but receives with a surface coil, and implement a filtered backprojection method of image acquisition and reconstruction. This part of the project is nearly complete. The coil pair has been built and tested successfully, and the MR scanner has been programmed to collect data to be reconstructed using the backprojection technique. To verify that multiexponential decays exist in the tumors, a simple spectroscopy experiment was performed on three rats implanted with breast tumors. Biexpenential decay was visible in the data, although there were large variations in the values of the decay constants, mostly likely due to susceptibility effects. This effect will not be a major factor in determining the relaxation parameters from the actual imaging experiments since spin echoes will be used for imaging.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1995
Accession Number
ADA302282

Entities

People

  • Hee K. Song

Organizations

  • University of Pennsylvania

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Animals
  • Biomedical Research
  • Breast Cancer
  • Cancer
  • Detection
  • Diseases And Disorders
  • Imaging Techniques
  • Laboratory Animals
  • Magnetic Resonance
  • Magnetic Resonance Imaging
  • Materials
  • Necrosis
  • Neoplasms
  • Nuclear Magnetic Resonance
  • Resonance
  • Standards

Fields of Study

  • Medicine
  • Physics

Readers

  • Electrochemical Engineering/ Fuel Cell Technologies
  • Image Processing and Computer Vision.
  • Medical Imaging.