Physiological noise reduction using volumetric functional magnetic resonance inverse imaging

Fa Hsuan Lin, Aapo Nummenmaa, Thomas Witzel, Jonathan R. Polimeni, Thomas A. Zeffiro, Fu Nien Wang*, John W. Belliveau

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    24 Citations (Scopus)


    Physiological noise arising from a variety of sources can significantly degrade the detection of task-related activity in BOLD-contrast fMRI experiments. If whole head spatial coverage is desired, effective suppression of oscillatory physiological noise from cardiac and respiratory fluctuations is quite difficult without external monitoring, since traditional EPI acquisition methods cannot sample the signal rapidly enough to satisfy the Nyquist sampling theorem, leading to temporal aliasing of noise. Using a combination of high speed magnetic resonance inverse imaging (InI) and digital filtering, we demonstrate that it is possible to suppress cardiac and respiratory noise without auxiliary monitoring, while achieving whole head spatial coverage and reasonable spatial resolution. Our systematic study of the effects of different moving average (MA) digital filters demonstrates that a MA filter with a 2 s window can effectively reduce the variance in the hemodynamic baseline signal, thereby achieving 57%-58% improvements in peak z-statistic values compared to unfiltered InI or spatially smoothed EPI data (FWHM = 8.6 mm). In conclusion, the high temporal sampling rates achievable with InI permit significant reductions in physiological noise using standard temporal filtering techniques that result in significant improvements in hemodynamic response estimation.

    Original languageEnglish
    Pages (from-to)2815-2830
    Number of pages16
    JournalHuman Brain Mapping
    Issue number12
    Publication statusPublished - Dec 2012
    MoE publication typeA1 Journal article-refereed


    • Event-related
    • FMRI
    • InI
    • Inverse imaging
    • Inverse solution
    • MRI
    • Neuroimaging
    • Visual


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