Abstract

Within the field of biomedicine, there exists a pronounced interest for longitudinal studies for development of novel treatments and for improving the understanding of disease progression and mechanisms. This should be of interest to be able to perform such studies using noninvasive, highly sensitive and relatively inexpensive systems. During the last few years, fluorescence diffuse optical imaging (FDOI) techniques have been developed to become an excellent tool for this purpose. This technique could, for example, be used to monitor the distribution and targeting of a drug on cancer tumors as well as the effects on the diseased tissue itself inside a small animal. The increasing interest of FDOI has also catalyzed the development of a framework for fluorescence diffuse optical tomography (FDOT) in order to provide 3D and also quantitative information. FDOT is a very powerful inverse method used to reconstruct an internal fluorophore distribution of a highly scattering material by acquiring the boundary fluence for multiple source-detector pairs. This is achieved by fitting the collected boundary data to a model, for example, the diffusion model. However, the problem is often very ill-posed due to the numerical nature of the problem formulation. It is hence of outmost importance to minimize any noise or background of the collected data. Although much of the noise can be eliminated by employing low-noise equipment, an intrinsic source of background known as the tissue autofluorescence remains to plague the measurements using traditional Stokes-shifting fluorophores. Autofluorescence is an intrinsic property of an object of interest, it cannot be easily corrected through, for example, background subtraction.

© 2009 OSA, IEEE Photonics Society, SPIE, COS, CIC

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