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.

We are presenting data from the largest clinical trial on optical tomographic imaging of finger joints to date. Overall we evaluated 99 fingers of patients affected by rheumatoid arthritis (RA) and 120 fingers from healthy volunteers. Using frequency-domain imaging techniques we show that sensitivities and specificities of 0.85 and higher can be achieved in detecting RA. This is accomplished by deriving multiple optical parameters from the optical tomographic images and combining them for the statistical analysis. Parameters derived from the scattering coefficient perform slightly better than absorption derived parameters. Furthermore we found that data obtained at 600 MHz leads to better classification results than data obtained at 0 or 300 MHz.

We investigate the performance of diffuse optical tomography to image highly heterogeneous media, such as breast tissue, as a function of background heterogeneity. To model the background heterogeneity, we have employed the functional information derived from Gadolinium-enhanced magnetic resonance images of the breast. We demonstrate that overall image quality and quantification accuracy worsens as the background heterogeneity increases. Furthermore we confirm the appearance of characteristic artifacts at the boundaries that scale with background heterogeneity. These artifacts are very similar to the ones seen in clinical examinations and can be misinterpreted as actual objects if not accounted for. To eliminate the artifacts and improve the overall image reconstruction, we apply a data-correction algorithm that yields superior reconstruction results and is virtually independent of the degree of the background heterogeneity.

Currently available tomographic image reconstruction schemes for optical tomography (OT) are mostly based on the limiting assumptions of small perturbations and a priori knowledge of the optical properties of a reference medium. Furthermore, these algorithms usually require the inversion of large, full, ill-conditioned Jacobian matrixes. In this work a gradient-based iterative image reconstruction (GIIR) method is presented that promises to overcome current limitations. The code consists of three major parts: 1) A finite-difference, time-resolved, diffusion forward model is used to predict detector readings based on the spatial distribution of optical properties; 2) An objective function that describes the difference between predicted and measured data; 3) An updating method that uses the gradient of the objective function in a line minimization scheme to provide subsequent guesses of the spatial distribution of the optical properties for the forward model. The reconstruction of these properties is completed, once a minimum of this objective function is found. After a presentation of the mathematical background, two- and three-dimensional reconstruction of simple heterogeneous media as well as the clinically relevant example of ventricular bleeding in the brain are discussed. Numerical studies suggest that intraventricular hemorrhages can be detected using the GIIR technique, even in the presence of a heterogeneous background.