Self-supervised deep-learned physics-informed PET image reconstruction for oncology

博士项目的目的开发不需要地面真实参考数据的自我监督深度学习PET重建方法开发快速单步运算符，用于从正弦图数据进行3D PET重建，以实现任何所需的目标功能开发对肿瘤学中飞行时间PET成像的适用性，提高图像质量估计重建不确定性图像正电子发射断层扫描（PET）是一种能够诊断癌症和监测治疗效果的医学成像模式。然而，PET图像通常受到噪声和低空间分辨率的限制，这可能限制看到小区域疾病的能力。最近，在图像重建中使用人工智能已经显著改善了PET图像质量，尽管使用传统的人工智能方法可能存在风险，这些方法需要从许多其他患者那里获取大量数据进行监督学习。本项目涉及利用先进的人工智能方法的特殊情况，特别是仅使用来自独特患者的采集数据的情况。与此同时，将探讨使用外部数据，至少是为了进行比较。该项目的目标是开发新的自监督图像重建方法，主要使用患者自己的数据提供AI优势，避免传统监督深度学习的一些陷阱。其动机是PET图像质量对患者的临床决策和治疗途径有影响。人工智能提供了明显的好处，但这些好处需要是强大的，主要依赖于患者自己的独特数据，并且需要向决策者明确重建图像的不确定性程度。

Aims of the PhD ProjectDevelop self-supervised deep-learning PET reconstruction methods which do not need ground truth reference dataDevelop fast single-step operators for 3D PET reconstruction from sinogram data for any desired objective functionDevelop applicability to time-of-flight PET imaging in oncology, improving image qualityEstimate reconstruction uncertainty imagesPositron emission tomography (PET) is a medical imaging modality that is able to diagnose cancer and monitor treatment efficacy. PET images are however often limited by noise and low spatial resolution, which can limit the ability to see small regions of disease. Recently, the use of AI within image reconstruction has offered notable improvements in PET image quality, although there can be risks with use of conventional AI methods which draw upon large volumes of data from many other patients for supervised learning.This project concerns the special case of harnessing advanced AI methodologies especially for the case of using only the acquired data from the unique patient. In tandem with this, use of external data will nonetheless be explored at least for comparison purposes. The goal though is for this project to develop novel self-supervised image reconstruction methods which deliver AI benefits using predominantly only the patient's own data, seeking to avoid some of the pitfalls of conventional supervised deep learning.The motivation is that PET image quality has an impact on clinical decision making and treatment pathways for patients. AI offers clear benefits, but these need to be robust benefits which rely mainly on a patient's own unique data and where the degree of uncertainty in the reconstructed images needs to be made clear to decision makers.