Dissertation/Thesis Abstract

Shape-based image reconstruction methods for hyperspectral diffuse optical tomography
by Larusson, Fridrik, Ph.D., Tufts University, 2013, 178; 3557529
Abstract (Summary)

Diffuse 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 method (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 reconstruction.

Indexing (document details)
Advisor: Miller, Eric L.
Commitee: Fang, Qianqian, Fantini, Sergio, Tracy, Brian
School: Tufts University
Department: Electrical Engineering
School Location: United States -- Massachusetts
Source: DAI-B 74/08(E), Dissertation Abstracts International
Subjects: Biomedical engineering, Electrical engineering, Medical imaging
Keywords: Breast imaging, Image reconstruction, Inverse problem, Photon migration, Tomography
Publication Number: 3557529
ISBN: 978-1-303-00672-2
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