Time-series estimation of biological factors in optical diffusion tomography

Abstract

We apply state space estimation techniques to the time-varying reconstruction problem in optical tomography. We develop a stochastic model for describing the evolution of quasi-sinusoidal medical signals such as the heartbeat, assuming these are represented as a known frequency with randomly varying amplitude and phase. We use the extended Kalman filter in combination with spatial regularization techniques to reconstruct images from highly under-determined time-series data. This system also naturally segments activity belonging to different biological processes. We present reconstructions of simulated data and of real data recorded from the human motor cortex (Franceschini et al 2000 Optics Express 6 49-57). It is argued that the application of these time-series techniques improves both the fidelity and temporal resolution of reconstruction in optical tomography.

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

“…A further class of approaches is based on state-space modeling using Kalman filtering (Abdelnour and Huppert, 2009;Diamond et al, 2005Diamond et al, , 2006Gagnon et al, 2011Gagnon et al, , 2014Kamrani et al, 2012;Kolehmainen et al, 2003;Prince et al, 2003) or recursive least-squares estimation (Aqil et al, 2012a,b). State-space methods model the data as a system with time-varying parameters that have to be estimated.…”

Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy

“…Barbour et al (2001) have long argued that diffuse optical imaging methods can provide rich physiological information through analysis of the systemic dynamic vascular signals. Prince et al (2003) have applied state space estimation techniques to the time-varying reconstruction to distinguish cardiac, respiratory, and brain activation signals. Zhang et al (in press) used a principle component analysis (PCA) to determine the principle spatial components of the spatial-temporal covariance of baseline optical data and then used it to filter systemic signal variation from optical data of brain activation.…”

Spectral Analysis of Event-Related Hemodynamic Responses in Functional Near Infrared Spectroscopy

“…Another useful note is that the inter-detector variability in the number of encountered Gamma matches decreases in the BC-band. These observations point to the fact that the cognitive activity is mainly localized above 0.03 Hz, that is, in the 30-300 mHz range Prince et al, 2003). The results are illustrated in Fig.…”

“…Efforts for characterizing the components in the spectra have concentrated on establishing a physiological correspondence with the peaks or the energy bands. Functional MRI and transcranial Doppler sonography studies have proposed several association mechanisms of vasomotor dynamics while emphasizing the fact that a strong signal, the brain hemodynamic response, exists and dominates the lower portion of the spectrum computed from temporal neuroimaging data (Obrig et al, 2000a;Franceschini et al, 2000;Prince et al, 2003;Toronov et al, 2000).…”