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Global motion detection and censoring in high-density diffuse optical tomography.

Arefeh Sherafati1, Abraham Z Snyder2,3, Adam T Eggebrecht2,4,5

  • 1Department of Physics, Washington University in St. Louis, St. Louis, Missouri, USA.

Human Brain Mapping
|July 11, 2020
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Summary
This summary is machine-generated.

A new motion detection method, global variance of temporal derivatives (GVTD), improves high-density diffuse optical tomography (HD-DOT) imaging by accurately identifying motion artifacts. This enhances spatial mapping accuracy compared to existing techniques.

Keywords:
functional near-infrared spectroscopyhigh-density diffuse optical tomographymotion artifactmotion censoringoptical neuroimaging

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Optical Physics

Background:

  • Motion artifacts significantly degrade optical neuroimaging quality, particularly in high-density diffuse optical tomography (HD-DOT).
  • Existing motion detection methods for HD-DOT are less developed than those for fMRI and fNIRS, limiting its application in challenging scenarios.
  • This necessitates advanced motion detection to expand HD-DOT's utility in diverse populations and settings.

Purpose of the Study:

  • To introduce and evaluate a novel motion detection method for multi-channel optical imaging systems.
  • To assess the efficacy of the global variance of temporal derivatives (GVTD) metric for identifying motion artifacts in HD-DOT.
  • To compare the performance of GVTD-based motion censoring against established methods in improving HD-DOT data quality and spatial mapping.

Main Methods:

  • Developed and implemented the global variance of temporal derivatives (GVTD) metric using spatial patterns across measurement channels.
  • Validated GVTD against external motion measures and assessed its sensitivity/specificity using instructed motion paradigms.
  • Applied GVTD-based motion censoring to resting-state and task-based HD-DOT data, comparing results with fMRI and other fNIRS motion correction techniques (CBSI, TDDR, wavelet filtering, tPCA).

Main Results:

  • GVTD demonstrated strong correlation with external motion measures and high accuracy in detecting instructed motion (AUC=0.88).
  • GVTD-based motion censoring significantly improved spatial similarity to fMRI maps for both resting-state and hearing words task HD-DOT data.
  • GVTD outperformed common fNIRS motion correction methods in producing spatial maps more consistent with fMRI.

Conclusions:

  • The GVTD metric is a sensitive and specific tool for detecting motion artifacts in multi-channel optical imaging.
  • GVTD-based motion censoring effectively enhances the quality and fMRI-comparability of HD-DOT data.
  • This advancement holds potential for broader application of HD-DOT in clinical and research settings, especially where motion is prevalent.