Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging

Abstract

Diffuse optical imaging is an effective technique for noninvasive functional brain imaging. However, the measurements respond to systemic hemodynamic fluctuations caused by the cardiac cycle, respiration, and blood pressure, which may obscure or overwhelm the desired stimulus-evoked response. Previous work on this problem employed temporal filtering, estimation of systemic effects from background pixels, or modeling of interference signals with predefined basis functions, with some success. However, weak signals are still lost in the interference, and other complementary methods are desirable. We use the spatial behavior of measured baseline signals to identify the interference subspaces. We then project signals components in this subspace out of the stimulation data. In doing so, we assume that systemic interference components will be more global spatially, with higher energy, than the stimulus-evoked signals of interest. Thus, the eigenvectors corresponding to the largest eigenvalues of an appropriate correlation matrix form the basis for an interference subspace. By projecting the data onto the orthogonal nullspace of these eigenvectors, we can obtain more localized response, as reflected in improved contrast-to-noise ratio and correlation coefficient maps.

A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex

“…There is the possibility that this problem can be helped by recording systemic signals and filtering them using adaptive filtering. Another promising spatial filtering method proposed by (Zhang et al, 2005b) may also be very useful in this regards. The results obtained in this study suggest that the low statistical significance of signals in the right side of the visual cortex are at least partly due to the larger distance between this part of the brain and the surface of the head.…”

Towards the next generation of near-infrared spectroscopy

“…Monitoring of oxygen saturation of the cerebral tissue has been performed with spatially resolved spectroscopy [41]. In addition to refining the measurements, mathematical methods, such as principal component analysis and independent component analysis, have also been used to remove extracerebral interference from NIRS signals [42][43][44]. In some cases, these methods have been successful in eliminating artefacts arising from a subject's physical and/or physiological activities, but further investigation is required to enable routine application to NIRS measurements.…”

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

“…These methods remove MAs by examining the characteristics of the signal [26][27][28][29][30][31][32][33]. One of these methods was developed by our group, the "movement artifact reduction algorithm" (MARA) [34].…”