Abstract
Fluorescence tomography is a non-invasive imaging modality that reconstructs fluorophore distributions inside a small animal from boundary measurements of the fluorescence light. The associated inverse problem is stabilized by a priori properties or information. In this paper, cases are considered where the fluorescent inclusions are well separated from the background and have a spatially constant concentration. Under these a priori assumptions, the identification process may be formulated as a shape optimization problem, where the interface between the fluorescent inclusion and the background constitutes the unknown shape. In this paper, we focus on the computation of the so-called topological derivative for fluorescence tomography which could be used as a stand-alone tool for the reconstruction of the fluorophore distributions or as the initialization in a level-set-based method for determining the shape of the inclusions
Original language | English |
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Article number | 025003 |
Number of pages | 30 |
Journal | Inverse Problems |
Volume | 29 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2013 |
Fields of Expertise
- Human- & Biotechnology
Treatment code (Nähere Zuordnung)
- Basic - Fundamental (Grundlagenforschung)