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Proceedings Paper

Recursive estimation methods for tracking of localized perturbations in absorption using diffuse optical tomography
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Paper Abstract

Analysis of the quasi-sinusoidal temporal signals measured by a Diffuse Optical Tomography (DOT) instrument can be used to determine both quantitative and qualitative characteristics of functional brain activities arising from visual and auditory simulations, motor activities, and cognitive tasks performances. Once the activated regions in the brain are resolved using DOT, the temporal resolution of this modality is such that one can track the spatial evolution (both the location and morphology) of these regions with time. In this paper, we explore a state-estimation approach using Extended Kalman Filters to track the dynamics of functionally activated brain regions. We develop a model to determine the size, shape, location and contrast of an area of activity as a function of time. Under the assumption that previously acquired MRI data has provided us with a segmentation of the brain, we restrict the location of the area of functional activity to the thin, cortical sheet. To describe the geometry of the region, we employ a mathematical model in which the projection of the area of activity onto the plane of the sensors is assumed to be describable by a low dimensional algebraic curve. In this study, we consider in detail the case where the perturbations in optical absorption parameters arising due to activation are confined to independent regions in the cortex layer. We estimate the geometric parameters (axis lengths, rotation angle, center positions) defining the best fit ellipse for the activation area's projection onto the source-detector plane. At a single point in time, an adjoint field-based nonlinear inversion routine is used to extract the activated area's information. Examples of the utility of the method will be shown using synthetic data.

Paper Details

Date Published: 11 March 2005
PDF: 12 pages
Proc. SPIE 5674, Computational Imaging III, (11 March 2005); doi: 10.1117/12.587837
Show Author Affiliations
Amine Hamdi, Northeastern Univ. (United States)
Massachusetts General Hospital, Harvard Medical School (United States)
Eric L. Miller, Northeastern Univ. (United States)
David Boas, Massachusetts General Hospital, Harvard Medical School (United States)
Maria Angela Franceschini, Massachusetts General Hospital, Harvard Medical School (United States)
Misha Elena Kilmer, Tufts Univ. (United States)


Published in SPIE Proceedings Vol. 5674:
Computational Imaging III
Charles A. Bouman; Eric L. Miller, Editor(s)

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