Monte Carlo study of global interference cancellation by multidistance measurement of near-infrared spectroscopy

• Published in: Journal of Biomedical Optics Vol. 14; no. 6; pp. 064025 - 0640210
• Main Authors: Umeyama, Shinji, Yamada, Toru
• Format: Journal Article
• Language: English
• Published: United States 01.11.2009
• Subjects: Adult; Algorithms; Brain - anatomy & histology; Brain - metabolism; Brain - physiology; Computer Simulation; Head - anatomy & histology; Head - physiology; Hemoglobins - metabolism; Humans; interference cancellation; Models, Anatomic; Models, Neurological; Monte Carlo Method; Monte Carlo simulation; multidistance measurement; near-infrared spectroscopy; Phantoms, Imaging; Reproducibility of Results; Spectroscopy, Near-Infrared - methods
• ISSN: 1083-3668; 1560-2281; 1560-2281
• DOI: 10.1117/1.3275466

The performance of near-infrared spectroscopy is sometimes degraded by the systemic physiological interference in the extracerebral layer. There is some systemic interference, which is highly correlated with the functional response evoked by a task execution. This kind of interference is difficult to remove by using ordinary techniques. A multidistance measurement method is one of the possible solutions for this problem. The multidistance measurement method requires estimation parameters derived from partial pathlength values of tissue layers to calculate an absorption coefficient change from a temporal absorbance change. Because partial path lengths are difficult to obtain, experimentally, we estimated them by a Monte Carlo simulation based on a five-layered slab model of a human adult head. Model parameters such as thickness and the transport scattering coefficient of each layer depend on a subject and a measurement position; thus, we assumed that these parameters obey normal distributions around standard parameter values. We determined the estimation parameters that provide a good separation performance in average for the model parameter distribution. The obtained weighting is robust to model parameter deviation and provides smaller errors on average compared to the parameters, which are determined without considering parameter distribution.