13 March 2017 Exploring the use of optical flow for the study of functional NIRS signals
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Abstract
Near infrared spectroscopy (NIRS) is an optical imaging technique that allows real-time measurements of Oxy and Deoxy-hemoglobin concentrations in human body tissue. In functional NIRS (fNIRS), this technique is used to study cortical activation in response to changes in neural activity. However, analysis of activation regions using NIRS is a challenging task in the field of medical image analysis and despite existing solutions, no homogeneous analysis method has yet been determined. For that reason, the aim of our present study is to report the use of an optical flow method for the analysis of cortical activation using near-infrared spectroscopy signals. We used real fNIRS data recorded from a noxious stimulation experiment as base of our implementation. To compute the optical flow algorithm, we first arrange NIRS signals (Oxy-hemoglobin) following our 24 channels (12 channels per hemisphere) head-probe configuration to create image-like samples. We then used two consecutive fNIRS samples per hemisphere as input frames for the optical flow algorithm, making one computation per hemisphere. The output from these two computations is the velocity field representing cortical activation from each hemisphere. The experimental results showed that the radial structure of flow vectors exhibited the origin of cortical activity, the development of stimulation as expansion or contraction of such flow vectors, and the flow of activation patterns may suggest prediction in cortical activity. The present study demonstrates that optical flow provides a power tool for the analysis of NIRS signals. Finally, we suggested a novel idea to identify pain status in nonverbal patients by using optical flow motion vectors; however, this idea will be study further in our future research.
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Raul Fernandez Rojas, Xu Huang, Keng-Liang Ou, Jesus Hernandez-Juarez, "Exploring the use of optical flow for the study of functional NIRS signals", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101372K (13 March 2017); doi: 10.1117/12.2254668; https://doi.org/10.1117/12.2254668
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