Application of a maximum likelihood algorithm to ultrasound modulated optical tomography.

In pulsed ultrasound modulated optical tomography (USMOT), an ultrasound (US) pulse performs as a scanning probe within the sample as it propagates, modulating the scattered light spatially distributed along its propagation axis. Detecting and processing the modulated signal can provide a 1-dimensional image along the US axis. A simple model is developed wherein the detected signal is modelled as a convolution of the US pulse and the properties (ultrasonic/optical) of the medium along the US axis. Based upon this model, a maximum likelihood (ML) method for image reconstruction is established. For the first time to our knowledge, the ML technique for an USMOT signal is investigated both theoretically and experimentally. The ML method inverts the data to retrieve the spatially varying properties of the sample along the US axis, and a signal proportional to the optical properties can be acquired. Simulated results show that the ML method can serve as a useful reconstruction tool for a pulsed USMOT signal even when the signal-to-noise ratio (SNR) is close to unity. Experimental data using 5 cm thick tissue phantoms (scattering coefficient µ(s) = 6.5 cm(-1), anisotropy factor g=0.93) demonstrate that the axial resolution is 160 µm and the lateral resolution is 600 µm using a 10 MHz transducer.

Image vector histogram approach to nanoparticle sizing.

A novel approach to measuring the size distribution of particles in the range of a few nanometers to a few micrometers is described. The method is based on processing multiple images of a sample of particles suspended in a liquid and undergoing Brownian motion. From each image, the centers of the particle positions are measured, then a histogram of the vectors connecting the centers in each image with all the centers in the next image is formed. This vector histogram contains information about the particle size distribution. A maximum-likelihood data inversion procedure to invert the data to yield a particle size distribution is described. Both computer simulation and experimental results are presented to demonstrate the effectiveness of the approach.

Improving visibility depth in passive underwater imaging by use of polarization.

Results are presented that demonstrate the effectiveness of using polarization discrimination to improve visibility when imaging in a scattering medium. The study is motivated by the desire to improve visibility depth in turbid environments, such as the sea. Most previous research in this area has concentrated on the active illumination of objects with polarized light. We consider passive or ambient illumination, such as that deriving from sunlight or a cloudy sky. The basis for the improvements in visibility observed is that single scattering by small particles introduces a significant amount of polarization into light at scattering angles near 90 degrees: This light can then be distinguished from light scattered by an object that remains almost completely unpolarized. Results were obtained from a Monte Carlo simulation and from a small-scale experiment in which an object was immersed in a cell filled with polystyrene latex spheres suspended in water. In both cases, the results showed an improvement in contrast and visibility depth for obscuration that was due to Rayleigh particles, but less improvement was obtained for larger scatterers.