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
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