Designing feedback-based contrast enhancement for in vivo imaging

MAGMA. 2006 Dec;19(6):333-46. doi: 10.1007/s10334-006-0061-z. Epub 2006 Dec 15.

Abstract

Objective: Nonlinear feedback interactions induced by the spins themselves have recently been introduced as novel MRI contrast enhancement mechanisms sensitive to small differences in MR parameters. Developing feedback-based contrast enhancement into a useful tool for in vivo imaging requires improved techniques that are robust to inhomogeneity and sensitive to subtle anatomical/physiological variations.

Materials and methods: Three different imaging methods combining the radiation damping feedback field with the distant dipolar field, applied radio-frequency (RF) fields, and local dipole fields, respectively, were designed and tested through numerical simulations on simple phantoms. These methods were demonstrated experimentally on live guppy fish, developing frog embryos, and blood in in vitro tissue samples by microimaging at 14.1 T.

Results: The developed feedback-based methods yielded images that identified distinct morphological features with superior contrast compared with conventional MR images and those acquired under radiation damping only. Positive contrast due to evolution under radiation damping and local dipole fields was also observed in SPIOs and blood.

Conclusion: Approaches to enhancing feedback-based contrast were successfully designed and demonstrated in vitro and in vivo. The newly devised methods were less sensitive to field inhomogeneity and prolonged evolution under the feedback fields, allowing for better visualization of contrast in vivo.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Contrast Media*
  • Feedback
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted

Substances

  • Contrast Media