Exploiting photon energy information in Positron Emission Tomography

在正电子发射断层扫描中利用光子能量信息

• 批准号: 2831470
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2831470
• 关键词: Exploiting; photon; energy; information; Positron

Positron Emission Tomography (PET) imaging forms a crucial part of managing the patient's treatment pathway in cancer and many other diseases. However, due to limitations on radioactive dose to the patient, PET images can be noisy and with relatively low image resolution. Modern PET scanners have experimental capabilities to measure gamma photon energy information in much more detail than before, which has the potential to increase signal to noise (SNR) ratio, as shown in preliminary scientific papers. However, further work is needed before this can be progressed to commercial systems.This project aims to evaluate the impact of various methods to exploit additional photon energy information on image quality, as well as optimise the most promising candidates. Aspects to be included are scatter rejection by using energy window tuning, photon scatter estimation, accidental coincidences, as well as image reconstruction strategies. We envisage combining established PET data processing with novel techniques using Machine Learning.This project will extend previous work on using photon energy in PET and SPECT. Previous publications have investigated the potential for a decrease in noise or the joint estimation of attenuation and emission images. We intend to combine this by investigating optimal energy windows providing a trade-off between accuracy and precision in the resulting estimates.This project fits primarily in the second Challenge of the EPSRC health technologies theme : Transforming early prediction and diagnosis.

正电子发射断层扫描（PET）成像构成了管理癌症和许多其他疾病的患者治疗途径的关键部分。但是，由于对患者的放射性剂量的限制，PET图像可能是嘈杂的，并且图像分辨率相对较低。现代PET扫描仪具有实验能力，可以比以前更详细地测量伽马光子能量信息，这有可能增加信号与噪声（SNR）比率，如初步科学论文所示。但是，在将其发展到商业系统之前，需要进一步的工作。该项目旨在评估各种方法的影响以利用其他光子能量信息对图像质量的影响，并优化最有希望的候选人。要包含的方面是使用能量窗口调整，光子散射估计，意外巧合以及图像重建策略来散布拒绝。我们设想将已建立的PET数据处理与使用机器学习的新技术相结合。该项目将扩展有关在PET和SPECT中使用Photon Energy的先前工作。先前的出版物已经调查了噪声减少或衰减图像的关节估计的潜力。我们打算通过调查最佳能源窗口来结合使用，从而在结果估计中提供精确度和精确度之间的权衡。该项目主要符合EPSRC Health Technologies主题的第二个挑战：转换早期预测和诊断。