Denoising Techniques
Lars Kasper1,2,3
1Techna Institute, University Health Network (UHN) Toronto, Toronto, ON, Canada, 2Translational Neuromodeling Unit, IBT, University of Zurich and ETH Zurich, Zurich, Switzerland, 3Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland

Synopsis

Noise is the eminent adversary when studying brain function. First, it incurs sensitivity loss for our small effects of interest by drowning them in un(cor)related fluctuations. Second, noise may correlate with effects, reducing specificity or increasing false positives by conflating them with fluctuations of non-neuronal origin. Here, we revisit how both the scanner and the subject generate noise in fMRI time series through different pathways, namely as thermal noise, encoding noise (magnetic field), and physiological noise of different origin.

We structure different approaches to noise mitigation following the recycling waste hierarchy, which also applies to sustainable science: avoid, reduce, reuse.

Target Audience

  • Any researcher performing or analyzing fMRI studies

Outcome/Objectives

  1. Understand noise mechanisms compromising fMRI data, such as thermal, encoding (magnetic field) and physiological noise (motion, cardiac and respiratory cycle)
  2. Avoid noise at the acquisition stage by suitable MR protocol, hardware and subject setup choices
  3. Reduce noise through image pre-processing techniques, through data-driven intrinsic correction methods (e.g., Independent Component Analysis, Global Signal Regression) or model-based approaches based on external recordings (e.g., RETROICOR, HRV and RVT)
  4. Reuse identified noise in statistical models to discern effects of neuronal origin from false positives
  5. Learn about latest developments in both data-driven (Aquino, Frederick) and mechanistic noise modeling (Mitsis)

Summary

Noise as "signal fluctuation of no interest" is the eminent adversary when studying brain function. First, it incurs sensitivity loss for our small effects of interest by drowning them in un(cor)related fluctuations. Second, noise may correlate with effects, reducing specificity or increasing false positives by conflating them with fluctuations of non-neuronal origin. Here, we revisit how both the scanner and the subject generate noise in fMRI time series through different pathways, namely as thermal noise, encoding noise (magnetic field [4]), and physiological noise of different origin (see reviews [6,13]). Specifically, the impact of motion [15-18], cardiac and respiratory physiology is scrutinized. We structure different approaches to noise mitigation following the recycling waste hierarchy, which also applies to sustainable science: avoid, reduce, reuse. Classical data-driven (ICA, FIX [20], AROMA [19], Global Signal Regression [1,8,14]) as well as model-based (RETROICOR [9], HRV [7], RVT, [2,3,11], toolboxes [5,10]) denoising techniques are discussed, as well as acquisition-specific variants (ME-ICA, [12]).
For more details, please see the recorded video and slides and refer to the listed references.

Acknowledgements

No acknowledgement found.

References

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Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)