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

该项目涉及创新的方法、先进的物理建模和深度学习的图像和信号处理，以推进癌症手术中最先进的术中探针。本研究项目将建立两个关键和互补方法的理论和实践理解：1)腹腔镜手术中癌症的体内检测和2)样本癌缘的离体检测。这可能涉及辐射物理（例如GEANT4）和探测器的蒙特卡罗模拟，调查干扰源，以及开发深度学习辅助图像和信号处理技术（例如在MATLAB/Python中），以便利用所有测量数据以获得最大的信息量。预计深度学习将极大地协助信号和图像处理，与分析或手工制作/直观的信号识别方法相比，可以增强电子和光子之间的区分。如果项目开始考虑成像，那么使用高质量的训练数据集对（低计数和高计数数据）进行深度学习，将通过使用生成建模实现强大的降噪，甚至有可能提高空间分辨率。该项目的范围从探头配置优化一直延伸到人工智能辅助实时手术成像能力的开发。探针设计优化将涉及实验和分析工作，以了解和评估用于检测和识别99mTc内部转换电子的方法和探测器。由于99mTc是核医学研究中使用最广泛的放射性核素，因此可以避免复杂的监管问题，因此该过程具有吸引力。这些电子的可靠检测、识别和定位带来了许多挑战，包括低内部转换（IC）电子产率、组织中的电子能量损失、伽马和电子背景（除了腹腔镜检查的限制外）。这些问题将通过实验和参考适当的模型来表征，最初是为了对所涉及的过程和权衡有一个很好的理解，随后是在可行的探针系统的背景下。实验将首先在现有的Lightpoint原型设备的背景下进行，该原型设备使用CMOS传感器直接对IC电子成像。然而，其他各种配置的探测器类型也可能是可行的，并将进行研究和评估

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