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，以推动癌症手术的医学成像技术的发展。癌症手术往往不成功，导致需要多次手术，或者增加了额外药物治疗或放射治疗的需要。例如，在接受前列腺癌手术的患者中，大约25%的患者手术切缘为阳性，这是癌症切除不完全的指标。手术经常不成功，因为外科医生缺乏在手术过程中实时检测癌症组织的工具。这一迫切的需求可以通过开发术中检测放射性药物示踪剂的技术来满足，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，以及由于需要收集和解释由于辐射吸收、散射效应和干扰的存在而产生的复杂信号而带来的物理挑战。这个Eng-D项目将建立对两种截然不同的方法的理论和实践的理解：在腹腔镜手术期间体内检测癌症和在体外样本上检测癌边缘。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以及开发信号处理和可视化技术(例如，MatLab/Python)。有希望的进展将通过实验、原型制作和使用医疗器械的真实数据和图像来进一步研究。这项工作将与工业界密切合作完成，学生将被安置在Lightpoint Medical。

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.