Global motion detection and censoring in high-density diffuse optical tomography
Autor: | Joseph P. Culver, Arefeh Sherafati, Adam T. Eggebrecht, Christopher D. Smyser, Tracy M. Burns-Yocum, Silvina L. Ferradal, Abraham Z. Snyder, Karla M. Bergonzi, Ben J.A. Palanca, Amy Robichaux-Viehoever, Tamara Hershey, Heather M. Lugar |
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Rok vydání: | 2020 |
Předmět: |
Male
Computer science optical neuroimaging Datasets as Topic 0302 clinical medicine Accelerometry Image Processing Computer-Assisted Research Articles Spectroscopy Near-Infrared motion censoring Radiological and Ultrasound Technology medicine.diagnostic_test 05 social sciences Brain Middle Aged Magnetic Resonance Imaging Neurology Censoring (clinical trials) Head Movements Principal component analysis Female Anatomy functional near‐infrared spectroscopy Artifacts Research Article Adult Sensitivity and Specificity high‐density diffuse optical tomography 050105 experimental psychology 03 medical and health sciences Young Adult Neuroimaging medicine Connectome Humans Tomography Optical 0501 psychology and cognitive sciences Radiology Nuclear Medicine and imaging motion artifact Aged Receiver operating characteristic Resting state fMRI business.industry Functional Neuroimaging Motion detection Pattern recognition Diffuse optical imaging Neurology (clinical) Artificial intelligence Functional magnetic resonance imaging business 030217 neurology & neurosurgery |
Zdroj: | Human Brain Mapping |
ISSN: | 1097-0193 |
Popis: | 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. |
Databáze: | OpenAIRE |
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