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Light scattering by tissue severely limits how deep beneath the surface one can image, and the spatial resolution one can obtain from these images. Diffuse optical tomography (DOT) is one of the most powerful techniques for imaging deep within tissue - well beyond the conventional  ∼ 10-15 mean scattering lengths tolerated by ballistic imaging techniques such as confocal and two-photon microscopy. Unfortunately, existing DOT systems are limited, achieving only centimeter-scale resolution. Furthermore, they suffer from slow acquisition times and slow reconstruction speeds making real-time imaging infeasible. We show that time-of-flight diffuse optical tomography (ToF-DOT) and its confocal variant (CToF-DOT), by exploiting the photon travel time information, allow us to achieve millimeter spatial resolution in the highly scattered diffusion regime ( mean free paths). In addition, we demonstrate two additional innovations: focusing on confocal measurements, and multiplexing the illumination sources allow us to significantly reduce the measurement acquisition time. Finally, we rely on a novel convolutional approximation that allows us to develop a fast reconstruction algorithm, achieving a 100× speedup in reconstruction time compared to traditional DOT reconstruction techniques. Together, we believe that these technical advances serve as the first step towards real-time, millimeter resolution, deep tissue imaging using DOT.

Abstract: Optical tomography belongs to the promising set of non-invasive methods for probing applications of semi-transparent media. This covers a wide range of fields. Nowadays, it is mainly driven by medical imaging in search of new less aggressive and affordable diagnostic means. This paper aims at presenting the most recent research accomplished in the authors’ laboratories as well as that of collaborative institutions concerning the development of imaging algorithms. The light transport modelling is not a difficult question as it used to be. Research is now focused on data treatment and reconstruction. Since the turn of the century, the rapid expansion of low cost computing has permitted the development of enhanced imaging algorithms with great potential. Some of these developments are already on the verge of clinical applications. This paper presents these developments and also provides some insights on still unresolved challenges. Intrinsic difficulties are identified and promising directions for solutions are discussed. [Copyright &y& Elsevier]