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Theoretical optimization of multi-echo fMRI data acquisition.

P A Gowland1, R Bowtell

  • 1Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, UK. penny.gowland@nottingham.ac.uk

Physics in Medicine and Biology
|March 22, 2007
PubMed
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Optimizing multi-echo functional MRI (fMRI) acquisition enhances BOLD signal detection. Simulations reveal optimal echo times and sampling periods, considering tissue properties and noise, to improve contrast-to-noise ratio for BOLD imaging.

Area of Science:

  • Magnetic Resonance Imaging
  • Neuroimaging
  • Biophysics

Background:

  • Functional magnetic resonance imaging (fMRI) relies on the blood-oxygen-level-dependent (BOLD) signal.
  • Optimizing data acquisition is crucial for maximizing BOLD signal detection sensitivity.
  • Tissue properties and physiological noise influence BOLD signal acquisition.

Purpose of the Study:

  • To optimize multi-echo fMRI acquisition parameters for enhanced BOLD signal detection.
  • To investigate the impact of tissue T*(2) variations on BOLD sensitivity.
  • To evaluate the effects of physiological noise and non-exponential signal decay on optimal acquisition.

Main Methods:

  • Simulations were employed to determine optimal sequence designs, including echo times (TE) and sampling periods.

Related Experiment Videos

  • Analysis considered variations in tissue T*(2), physiological noise, and signal decay characteristics.
  • The contrast-to-noise ratio (CNR) was used as a metric for optimization.

    • Main Results:

    • For single-echo acquisition, the optimal sampling period is up to 3T*(2) with an optimum TE of 1.5T*(2).
    • Multi-echo acquisition with weighted summation improves CNR, with shorter optimum echo intervals.
    • Sensitivity to BOLD signal changes varies with tissue T*(2) in multi-echo acquisitions, favoring tissues with shorter T*(2) when optimized for a specific T*(2).

    Conclusions:

    • Multi-echo fMRI acquisition can be optimized to improve BOLD signal detection, but sensitivity is T*(2)-dependent.
    • Fitting for DeltaR*(2) reduces CNR, necessitating the use of short echo time intervals and numerous echoes.
    • Optimal sequence parameters demonstrate robustness against physiological noise contributions.