Exploiting photon energy information in Positron Emission Tomography

正电子发射断层扫描（PET）成像是管理癌症和许多其他疾病患者治疗途径的关键部分。然而，由于对患者的放射性剂量的限制，PET图像可能有噪声并且具有相对低的图像分辨率。现代PET扫描仪具有比以前更详细地测量伽马光子能量信息的实验能力，这有可能增加信噪比（SNR），如初步科学论文所示。然而，在这可以进展到商业系统之前，还需要进一步的工作。该项目旨在评估各种方法的影响，以利用额外的光子能量信息对图像质量，以及优化最有前途的候选人。要包括的方面是通过使用能量窗口调整，光子散射估计，偶然巧合，以及图像重建策略的散射抑制。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。该项目将扩展以前在PET和SPECT中使用光子能量的工作。以前的出版物已经研究了减少噪声或联合估计衰减和发射图像的潜力。我们打算通过研究最佳能量窗口来联合收割机，从而在最终估计的准确性和精确度之间进行权衡。该项目主要适合EPSRC卫生技术主题的第二个挑战：改变早期预测和诊断。

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.