Time-series estimation of biological factors in optical diffusion tomography.
PHYS MED BIOL
1491 - 1504.
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.
|Title:||Time-series estimation of biological factors in optical diffusion tomography|
|Keywords:||ELECTRICAL-IMPEDANCE TOMOGRAPHY, NEAR-INFRARED SPECTROSCOPY, DYNAMIC INVERSE PROBLEMS, FINITE-ELEMENT-METHOD, IN-PROCESS TOMOGRAPHY, IMAGE-RECONSTRUCTION, SCATTERING MEDIA, STATE-ESTIMATION, EFFICIENT ALGORITHMS, HUMAN BRAIN|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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