Cancer is one of the leading causes of mortality worldwide and its incidence is on the rise. The current diagnostic pathways are highly invasive, with associated risks, and are prone to misclassifications. Magnetic Resonance Imaging (MRI) is increasingly being used for non-invasive cancer detection. However, standard MRI cannot characterise the aggressiveness of cancer, which is determined by features like the size, shape and density of the cancer cells and is crucial for cancer management. Diffusion MRI (DMRI) is sensitive to the microstructural properties of tissue and thus is a promising tool for characterisation of cancer.
Getting clinically useful measures from DMRI signal requires a model to describe how the measured DMRI signal depends on the microstructure, which varies for the different cancer pathologies. In this project, you will fit a variety of models to DMRI data, acquired on cancer patients and quantify which model best describe the data within the cancerous tissue. The best model can then be used to quantify and compare the microstructure estimates made across the tissue, for example, to highlight lesions with varying cancer aggressiveness.