Biomedical imaging and signal processing for the intra-operative detection of cancerous tissue
This industry led project involves the application of signal processing and optical engineering to advance the state of the art in medical imaging for cancer surgery. Cancer surgery is often unsuccessful, resulting in the need for multiple operations or increasing the need for additional drug treatment or radiotherapy. For example, approximately 25% of patients undergoing surgery for prostate cancer will have a positive surgical margin which is an indicator of incomplete cancer removal. Surgery is unsuccessful so often because surgeons lack a tool to detect cancerous tissue in real time during surgery. This pressing need can be met through the development of intra-operative technology for detecting radiopharmaceutical tracers which are currently used for pre-operative PET and SPECT scans. These novel techniques face engineering challenges due to the time, space and activity concentration constraints of the application as well as physics challenges due to the need to collect and interpret complex signals due to radiation absorption, scattering effects and the presence of interferences. This Eng-D project will build both a theoretical and practical understanding of two contrasting approaches; in-vivo detection of cancer during laparoscopic surgery and detection of cancerous margins on ex-vivo samples. This will involve Monte Carlo simulations of the radiation physics (e.g. GEANT4), investigating sources of interference in practice and developing signal processing and visualisation techniques (e.g. MATLAB/Python). Promising advances will be further investigated through experiment, prototyping and using real data and images from medical instruments. This work will be done in close collaboration with industry, with the student being placed at Lightpoint Medical.
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