A new multiple lifetime fitting algorithm is presented which deconvolves a time-domain system Instrument Response
Function (IRF) from a measured Fluorescence Time Point Spread Function (FTPSF) prior to lifetime fitting.
Deconvolution is followed by filtering, using a special case of the optimal Wiener filter, where the signal-to-noise ratio
(SNR) in the spectral domain is evaluated empirically, and thus tuned with respect to each specific FTPSF-IRF
combination at hand. Comparisons between the proposed deconvolution scheme and the classical Iterative Convolution
(IC) scheme over a set of simulated and experimental data reveal that the proposed scheme typically exhibits
order-of-magnitude performance gains (accuracy and efficiency combined) over the IC scheme in realistic conditions.
In order to precisely recover fluorescence lifetimes from bulk tissues, one needs to employ complex light propagation
models (e.g., the radiative transfer equation or a simpler yet consistent approximation, the diffusion equation) requiring
knowledge of the tissue optical properties. This can be computationally expensive and therefore not practical in many
applications. We present a novel method to estimate the fluorescence lifetimes of multiple fluorophores embedded in
mice. By assuming that the photon diffusion does not significantly change the fluorescence decay slope, the light
propagation is simply modeled as a time-delay during lifetime estimation. Applications of this approach are
demonstrated by simulation, phantom data, and in vivo experiments.
Optical properties heterogeneities in small-animals can deeply affect diffuse optical fluorescence data. This can severely
limit the precision of fluorescence tomography when the forward model is built assuming homogeneous absorption and
scattering coefficients. In this work, we introduce a photon propagation forward model in which local estimates of a
sample's optical properties are used for each source-detector combination, rather than a single global estimate of those
optical properties. These estimates may be obtained from either time-resolved data collected at the laser's wavelength, or
based on a priori information gained through another imaging modality. We show that without increasing the
computational complexity, our model improves the correlation with independently simulated heterogeneous fluorescence
data in the case of optical property heterogeneity levels typically observed in mice.
We present an algorithm using data acquired with a time-resolved system with the goal of reconstructing sources of fluorescence
emanating from the deep interior of highly scattering biological tissues. A novelty in our tomography algorithm is the integration of a
light transport model adapted to rodent geometries. For small volumes, our analysis suggest that neglecting the index of refraction
mismatch between diffusive and non-diffusive regions, as well as the curved nature of the boundary, can have a profound impact on
fluorescent images and spectroscopic applications relying on diffusion curve fitting. Moreover, we introduce a new least-squares
solver with bound constraints adapted for optical problems where a physical non-negative constraint can be imposed. Finally, we find
that maximizing the time-related information content of the data in the reconstruction process significantly enhances the quality of
fluorescence images. Preliminary noise propagation and detector placement optimization analysis are also presented.
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