Physiological noise reduction using volumetric functional magnetic resonance inverse imaging

Research output: Contribution to journalArticleScientificpeer-review


  • Fa-Hsuan Lin

  • Aapo Nummenmaa
  • Thomas Witzel
  • Jonathan R. Polimeni
  • Thomas A. Zeffiro
  • Fu Nien Wang
  • John W. Belliveau

Research units

  • National Taiwan University
  • Harvard University
  • National Tsing Hua University


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

    Research areas

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

ID: 13555545