Biomedical imaging and signal processing for the intra-operative detection of cancerous tissue

这个行业主导的项目涉及信号处理和光学工程的应用，以推进癌症手术医学成像的最新技术。癌症手术往往不成功，导致需要多次手术或增加额外的药物治疗或放疗的需要。例如，大约25%接受前列腺癌手术的患者手术切缘呈阳性，这是癌症切除不完全的一个指标。手术常常不成功，因为外科医生在手术过程中缺乏实时检测癌组织的工具。这一迫切需求可以通过术中检测放射性药物示踪剂技术的发展来满足，这些示踪剂目前用于术前PET和SPECT扫描。由于应用的时间、空间和活动浓度的限制，这些新技术面临着工程上的挑战，同时由于辐射吸收、散射效应和干扰的存在，需要收集和解释复杂信号，这些技术也面临着物理上的挑战。这个英语- d项目将建立对两种对比方法的理论和实践理解；腹腔镜手术中肿瘤的体内检测和离体样本癌缘的检测。这将涉及辐射物理的蒙特卡罗模拟（例如GEANT4），调查实践中的干扰源，开发信号处理和可视化技术（例如MATLAB/Python）。通过实验、原型制作和使用医疗器械的真实数据和图像，将进一步研究有希望的进展。这项工作将与工业界密切合作，学生将被安置在Lightpoint医疗公司。

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.