A Time-Domain Wavelet-Based Approach for Fluorescence Diffuse Optical Tomography

Purpose: In the context of fluorescence diffuse optical tomography, determining the optimal way to exploit the time-resolved information has been receiving much attention and different features of the time-resolved signals have been introduced. In this article, the authors revisit and generalize the notion of feature, considering the projection of the measurements onto some basis functions. This leads the authors to propose a novel approach based on the wavelet transform of the measurements. Methods: A comparative study between the reconstructions obtained from the proposed wavelet-based approach and the reconstructions obtained from the reference temporal moments is provided. An inhomogeneous cubic medium is considered. Reconstructions are performed from synthetic measurements assuming Poisson noise statistics. In order to provide fairly comparable reconstructions, the reconstruction scheme is associated with a particular procedure for selecting the regularization parameter. Results: In the noise-free case, the reconstruction quality is shown to be mainly driven by the number of selected features. In the presence of noise, however, the reconstruction quality depends on the type of the features. In this case, the wavelet approach is shown to outperform the moment approach. While the optimal time-resolved reconstruction quality, which is obtained considering the whole set of time samples, is recovered using only eight wavelet functions, it cannot be attained using moments. It is finally observed that the time-resolved information is of limited utility, in terms of reconstruction, when the maximum number of detected photons is lower than 105. Conclusions: The wavelet approach allows for better exploiting the time-resolved information, especially when the number of detected photons is low. However, when the number of detected photons decreases below a certain threshold, the time-resolved information itself is shown to be of limited utility.