Shape-based image reconstruction methods for hyperspectral diffuse optical tomography.
optical tomography (DOT) is an optical imaging modality that uses near infrared light to
recover functional information of tissue. In this thesis we focus on breast imaging
where estimation of the optical properties of the breast can assist in detecting
cancerous tumors and in judging overall breast health. To this end we explore the
application of a parametric level set ... read moremethod (PaLS) for image reconstruction for
hyperspectral DOT. Chromophore concentrations and diffusion amplitude are recovered
using a linearized Born approximation model and employing data from over 100
wavelengths. The images to be recovered are taken to be piecewise constant and a newly
introduced, shape-based model is used as the foundation for reconstruction. The PaLS
method significantly reduces the number of unknowns relative to more traditional
level-set reconstruction methods and has been shown to be particularly well suited for
ill-posed inverse problems such as the one of interest here. We extend the PaLS method
to imaging problems by considering a redundant dictionary matrix for basis functions
allowing for recovery of a wide array of shapes. Additionally we explore the ability of
diffuse optical tomography (DOT) to recover 3D tubular shapes representing vascular
structures in breast tissue. Using the PaLS method, we incorporate the connectedness of
vascular structures in breast tissue to reconstruct shape and absorption values from
severely limited data sets. The approach is based on a decomposition of the unknown
structure into a series of two dimensional slices. Using a simplified physical model
that ignores 3D effects of the complete structure, we develop a novel inter-slice
regularization strategy to obtain global regularity. We report on simulated and
experimental reconstructions using realistic optical contrasts where our method provides
a more accurate estimation compared to an unregularized approach and a pixel based
Thesis (Ph.D.)--Tufts University, 2013.
Submitted to the Dept. of Electrical Engineering.
Advisor: Eric Miller.
Committee: Sergio Fantini, Brian Tracy, and Qianqian Fang.
Keywords: Electrical engineering, and Biomedical engineering.read less