Open AccessGlobal motion detection and censoring in high‐density diffuse optical tomography
Author(s) -
Sherafati Arefeh,
Snyder Abraham Z.,
Eggebrecht Adam T.,
Bergonzi Karla M.,
BurnsYocum Tracy M.,
Lugar Heather M.,
Ferradal Silvina L.,
RobichauxViehoever Amy,
Smyser Christopher D.,
Palanca Ben J.,
Hershey Tamara,
Culver Joseph P.
Publication year - 2020
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25111
Subject(s) - artificial intelligence , diffuse optical imaging , functional magnetic resonance imaging , computer science , computer vision , pattern recognition (psychology) , neuroimaging , censoring (clinical trials) , correlation , optical flow , iterative reconstruction , mathematics , psychology , statistics , neuroscience , geometry , image (mathematics)
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 imaging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluated a new motion detection method for multi‐channel optical imaging systems that leverages spatial patterns across measurement channels. Specifically, we introduced a global variance of temporal derivatives (GVTD) metric as a motion detection index. We showed that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion—with an area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we showed that applying GVTD‐based motion censoring on both hearing words task and resting state HD‐DOT data with natural head motion results in an improved spatial similarity to fMRI mapping. We then compared 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 on HD‐DOT data outperforms other methods and results in spatial maps more similar to those of matched fMRI data.