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 , anisotropy factor ) demonstrate that the axial resolution is 160 m and the lateral resolution is 600 m using a 10 MHz transducer.