The interplay between engineering and medical research plays a major role in advancing
the healthcare technologies. Novel medical devices have been developed to improve the
diagnosis and treatment plans for patients with pathological conditions such as prostate
cancer (PCa). In this context, in silico modelling has been a valuable tool as it is
complementary to traditional trial-and-error approaches, particularly in the area of nodule
identification in soft tissue. The goal of this thesis is to develop a computational
framework of detecting and characterizing the presence of PCa, based on instrumented
probing. The proposed methodologies involve Finite-Element simulations, inverse
analysis and probability-based methods, using models reconstructed from medical
imaging and histological data. The proposed methods are later validated using
experimental measurements from instrumented probing on ex-vivo prostates. It is
expected that the in-silico framework can serve as a complementary tool to the medical
devices and to improve the effectiveness of current methods for early PCa diagnosis.James-Watt ScholarshipHeriot-Watt University - Annual Fund Gran
