Global Motion Detection and Censoring in High-Density Diffuse Optical Tomography

Abstract

Motion-induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high-density diffuse optical tomography (HD-DOT) with hundreds to thousands of source-detector pair measurements, motion detection methods are underdeveloped relative to both functional magnetic resonance imaging (fMRI) and standard functional near-infrared spectroscopy (fNIRS). This limitation restricts the application of HD-DOT in many challenging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluate a new motion detection method for multichannel optical imaging systems that leverages spatial patterns across channels. Specifically, we introduce a global variance of temporal derivatives (GVTD) metric as a motion detection index. We show that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion - with area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we show that applying GVTD-based motion censoring on both task and resting state HD-DOT data with natural head motion results in an improved spatial similarity to fMRI mapping for the same respective protocols (task or rest). We then compare the GVTD similarity scores with several commonly used motion correction methods described in the fNIRS literature, including correlation-based signal improvement (CBSI), temporal derivative distribution repair (TDDR), wavelet filtering, and targeted principal component analysis (tPCA). We find that GVTD motion censoring outperforms other methods and results in spatial maps more similar to matched fMRI data.