title: 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.
authors.affiliation: Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 113576. ucacarr@cs.ucl.ac.uk
authors.forename: Simon
authors.initials: S
authors.lastname: Prince
authors.forename: Ville
authors.initials: V
authors.lastname: Kolehmainen
authors.forename: Jari P
authors.initials: JP
authors.lastname: Kaipio
authors.forename: Maria Angela
authors.initials: MA
authors.lastname: Franceschini
authors.forename: David
authors.initials: D
authors.lastname: Boas
authors.forename: Simon R
authors.initials: SR
authors.lastname: Arridge
mesh_terms: D000465:Algorithms; D004058:Diffusion; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009044:Motor Cortex / Q000098:blood supply* / Q000502:physiology*; D009991:Oscillometry / Q000379:methods*; D010507:Periodicity; D018401:Sample Size; D013269:Stochastic Processes; D013997:Time Factors; D041622:Tomography, Optical / Q000379:methods*