This paper discusses instrumentation based on multiview parallel high temporal resolution (<50 ps) time-domain (TD) measurements for diffuse optical tomography (DOT) and a prospective view on the steps to undertake as regards such instrumentation to make TD-DOT a viable technology for small animal molecular imaging. TD measurements provide information-richest data, and we briefly review the interaction of light with biological tissues to provide an understanding of this. This data richness is yet to be exploited to its full potential to increase the spatial resolution of DOT imaging and to allow probing, via the fluorescence lifetime, tissue biochemical parameters, and processes that are otherwise not accessible in fluorescence DOT. TD data acquisition time is, however, the main factor that currently compromises the viability of TD-DOT. Current high temporal resolution TD-DOT scanners simply do not integrate sufficient detection channels. Based on our past experience in developing TD-DOT instrumentation, we review and discuss promising technologies to overcome this difficulty. These are single photon avalanche diode (SPAD) detectors and fully parallel highly integrated electronics for time-correlated single photon counting (TCSPC). We present experimental results obtained with such technologies demonstrating the feasibility of next-generation multiview TD-DOT therewith.
An imaging algorithm is implemented for tomographically reconstructing contrast maps of the space variant speed of diffuse photon density wavefronts (DPDWFs) propagating in biological tissue-like diffusing media. This speed serves as a novel contrast not previously exploited in the literature. The algorithm employs early photon arrival times (EPATs) extracted from a set of time domain measurements. A relationship between EPATs and the speed of DPDWFs is exploited as the forward model. The forward model and its use in an inverse problem are supported by experimental results. These are carried out for 3D media with tissue-like optical properties. The resulting inverse problem is formulated as a set of algebraic equations and solved within a constrained linear least squares framework. The results indicate that the algorithm provides tomographic information on heterogeneities locations and distributions.
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