Monte Carlo modelling of Raman scattering in heterogeneous breast tissue

Nearly 60,000 women are diagnosed with breast cancer each year in the UK, and there are 12,000 deaths. Early diagnosis is key, with 90% of women diagnosed at the earliest stage surviving for at least five years, compared to 15% for women diagnosed with the most advanced stage. The focus of this research project, deep Raman spectroscopy, offers the possibility of real-time, in vivo diagnosis of breast cancer.

Raman spectroscopy is an optical technique used to identify chemicals in a sample via inelastic scattering from vibrating molecules. Differences in the Raman signal from cancerous and normal tissue has already been used to identify early epithelial cancer, but until recently the technique has been limited to the tissue surface at depths of about 1 mm. Prof Stone's group has now demonstrated that by using deep Raman spectroscopy techniques depths of several centimetres may be probed, bringing subsurface cancers into range.

We wish to obtain a detailed, quantitative understanding of the sensitivity and specificity of Raman breast cancer diagnosis via state-of-the-art numerical simulations. The project will use software originally developed to model light transport through gas and dust in space, that has been adapted to model how light propagates through tissue. The code uses the Monte Carlo method, in which the illuminating radiation is modelled as a large number of photon packets, is ideally suited to highly scattering media such as human tissue.

A key requirement for a reliable simulation is a realistic model for the optical properties of the scattering medium. The heterogeneous nature of tissue is usually simply described using multiple plane-parallel homogeneous layers. Our code, which combines triangular mesh surfaces with an adaptive Cartesian mesh, enables a much more sophisticated realization of the tissue's true three-dimensional structure.

The initial aim of this project is to implement Raman scattering physics into the code and validate this using extant experimental data. Subsequently a breast model will be constructed based on MRI data segmented to identify the different tissue types. This heterogeneous breast tissue model will provide an environment within which numerical experiments may be performed to quantify the sensitivity and specificity of Raman spectroscopy to varying tissue properties, cancer distributions, and probe geometries. The outputs from this project will feed back directly into the experimental work Prof Stone's group, with ultimate aim of establishing deep Raman spectroscopy as a routine diagnostic method in the clinical environment.