Global motion detection and censoring in high‐density diffuse optical tomography
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 m...
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| Published in | Human brain mapping Vol. 41; no. 14; pp. 4093 - 4112 |
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| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.10.2020
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| 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 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.
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). Here, we evaluated a new motion detection method for multi‐channel optical imaging systems that leverages spatial patterns across channels. Specifically, we introduced a global variance of temporal derivatives (GVTD) metric as a motion detection index and showed that it strongly correlates with external measures of motion. |
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| AbstractList | 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.
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). Here, we evaluated a new motion detection method for multi‐channel optical imaging systems that leverages spatial patterns across channels. Specifically, we introduced a global variance of temporal derivatives (GVTD) metric as a motion detection index and showed that it strongly correlates with external measures of motion. 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. 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.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. |
| Audience | Academic |
| Author | Hershey, Tamara Ferradal, Silvina L. Culver, Joseph P. Snyder, Abraham Z. Palanca, Ben J. Burns‐Yocum, Tracy M. Lugar, Heather M. Smyser, Christopher D. Eggebrecht, Adam T. Robichaux‐Viehoever, Amy Bergonzi, Karla M. Sherafati, Arefeh |
| AuthorAffiliation | 6 L3Harris, 400 Initiative Dr Rochester New York 14624 USA 1 Department of Physics Washington University in St. Louis St. Louis Missouri USA 11 Department of Anesthesiology Washington University School of Medicine in St. Louis, St. Louis Missouri USA 8 Department of Psychiatry Washington University School of Medicine in St. Louis St. Louis Missouri USA 2 Department of Radiology Washington University School of Medicine in St St. Louis Missouri USA 4 Department of Biomedical Engineering Washington University School in St. Louis St. Louis Missouri USA 3 Department of Neurology Washington University in St. Louis St. Louis Missouri USA 9 Department Of Intelligent Systems Engineering Indiana University Bloomington Indiana USA 10 Department of Pediatrics Washington University in St. Louis St. Louis Missouri USA 5 Division of Biology and Biomedical Sciences Washington University School of Medicine in St. Louis St. Louis Missouri USA 7 Department of Psychological and Brain Sciences Indiana University Blo |
| AuthorAffiliation_xml | – name: 5 Division of Biology and Biomedical Sciences Washington University School of Medicine in St. Louis St. Louis Missouri USA – name: 11 Department of Anesthesiology Washington University School of Medicine in St. Louis, St. Louis Missouri USA – name: 2 Department of Radiology Washington University School of Medicine in St St. Louis Missouri USA – name: 1 Department of Physics Washington University in St. Louis St. Louis Missouri USA – name: 7 Department of Psychological and Brain Sciences Indiana University Bloomington Indiana USA – name: 4 Department of Biomedical Engineering Washington University School in St. Louis St. Louis Missouri USA – name: 8 Department of Psychiatry Washington University School of Medicine in St. Louis St. Louis Missouri USA – name: 9 Department Of Intelligent Systems Engineering Indiana University Bloomington Indiana USA – name: 6 L3Harris, 400 Initiative Dr Rochester New York 14624 USA – name: 3 Department of Neurology Washington University in St. Louis St. Louis Missouri USA – name: 10 Department of Pediatrics Washington University in St. Louis St. Louis Missouri USA |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32648643$$D View this record in MEDLINE/PubMed |
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| Keywords | optical neuroimaging motion censoring functional near-infrared spectroscopy high-density diffuse optical tomography motion artifact |
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| Snippet | Motion‐induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high‐density diffuse optical tomography... Motion-induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high-density diffuse optical tomography... |
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| SubjectTerms | Accelerometry Adult Aged Brain - diagnostic imaging Brain mapping Censorship Connectome - standards Correlation analysis Datasets as Topic Density Detectors Evaluation Female Functional magnetic resonance imaging functional near‐infrared spectroscopy Functional Neuroimaging - standards Head movement Head Movements high‐density diffuse optical tomography Humans Image Processing, Computer-Assisted - standards Imaging systems Infrared spectroscopy Magnetic resonance imaging Magnetic Resonance Imaging - standards Male Mapping Mathematical analysis Measurement methods Medical imaging Middle Aged motion artifact motion censoring Motion detection Motion perception Neuroimaging optical neuroimaging Principal components analysis Sensitivity and Specificity Similarity Spectroscopy, Near-Infrared - standards Tomography Tomography, Optical - standards Young Adult |
| Title | Global motion detection and censoring in high‐density diffuse optical tomography |
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