'Intra-operative probe design and image processing optimisation with deep learning for in-vivo and ex-vivo detection of cancerous tissue'

Lead Research Organisation: King's College London
Department Name: Imaging & Biomedical Engineering


This project involves innovative methodologies, advanced physics modelling and deep-learned image and signal processing to advance the state-of-the-art in intra-operative probes for cancer surgery.

This research project will build both a theoretical and practical understanding of two key and complementary approaches: 1) in-vivo detection of cancer during laparoscopic surgery and 2) ex-vivo detection of cancerous margins on samples. This will likely involve Monte Carlo simulations of the radiation physics (e.g. GEANT4) and the probe, investigating sources of interference, as well as developing deep-learning assisted image and signal processing techniques (e.g. in MATLAB/Python) in order to exploit all measured data to its fullest informative extent.

It is anticipated that deep learning will greatly assist in the signal and image processing, allowing enhanced discrimination between electrons and photons compared to analytic or hand-crafted / intuitive methods for signal discrimination.

If the project starts to consider imaging, then use of high quality training dataset pairs (low count and high count data) with deep learning will allow powerful noise reduction, and even the possibility of enhanced spatial resolution, through use of generative modelling.
The project has scope to extend all the way from probe configuration optimisation through to development of AI-assisted real-time surgical imaging capabilities.

Probe design optimisation will involve experimental and analysis work to understand and evaluate methods and detectors for the detection and identification of internal conversion electrons from 99mTc. This process is attractive as 99mTc is the most widely used radionuclide for nuclear medicine studies, and so complex regulatory issues can be avoided. Reliable detection, identification and localisation these electrons poses many challenges associated with low internal conversion (IC) electron yield, electron energy loss in tissue, and gamma and electron background (in addition to the constraints of laparoscopy). These issues will be characterised experimentally and with reference to appropriate models, initially to obtain a good understanding of the processes and trade-offs involved and subsequently in the context of workable probe systems. Experiments will be performed initially in the context of an existing Lightpoint prototype device that uses a CMOS sensor to directly image the IC electrons. However other detectors types in various configurations may also be feasible and will be investigated and evaluated


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R513064/1 01/10/2018 30/09/2023
2272219 Studentship EP/R513064/1 01/10/2019 31/07/2023 Joshua Moo