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

这项研究项目包括创新的方法、先进的物理模型和深入学习的图像和信号处理，以推进癌症手术中探查的最先进水平。这项研究项目将建立对两种关键和互补方法的理论和实践理解：1)在腹腔镜手术中在体内检测癌症；2)体外检测样本上的癌边缘。这可能涉及对辐射物理(例如GEANT4)和探测器进行蒙特卡罗模拟，调查干扰源，以及开发深度学习辅助的图像和信号处理技术(例如在MatLab/Python中)，以便最大限度地利用所有测量数据。预计深度学习将极大地帮助信号和图像处理，与用于信号识别的解析或手工/直观方法相比，允许增强电子和光子之间的区分。如果项目开始考虑成像，则使用具有深度学习的高质量训练数据集对(低计数和高计数数据)将允许强大的噪声降低，甚至增强空间分辨率的可能性，通过使用生成性建模，该项目的范围将从探测器结构优化一直延伸到人工智能辅助实时外科成像能力的开发。可能性设计优化将涉及实验和分析工作，以了解和评估从99mTc探测和识别内部转换电子的方法和探测器。这一过程很有吸引力，因为99mTc是用于核医学研究的最广泛使用的放射性核素，因此可以避免复杂的监管问题。这些电子的可靠检测、识别和定位带来了许多挑战，包括低的内部转换(IC)电子产额、组织中的电子能量损失以及伽马和电子背景(此外还有腹腔镜检查的限制)。这些问题将通过实验和参考适当的模型来描述，最初是为了更好地了解所涉及的过程和权衡，随后是在可行的探测系统的背景下。实验最初将在现有的LightPoint原型设备的背景下进行，该设备使用一个cmos传感器来直接成像IC电子。然而，各种配置的其他类型的检测器也可能是可行的，并将被调查和评估

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