Fully Bayesian 3D PET-MR Neuroimaging Reconstruction

PHD项目的目的开发一种用于脑PET的PET-MR潜在空间生成性建模方法为不确定图像提供重建减少噪声和提高脑PET图像的空间分辨率，潜在地降低注入辐射剂量或缩短扫描时间正电子发射断层扫描(PET)被广泛用于癌症和心脏和大脑疾病的成像。该项目涉及使用同步PET-MR扫描仪进行脑成像的情况，在研究和临床成像方面具有潜在的应用前景。脑PET成像可能会受到噪声数据和相对较低的空间分辨率的限制，这取决于所管理的放射性剂量和使用的放射性示踪剂。该项目将使用人工智能方法来最好地利用额外的信息来帮助提高图像质量，例如来自同时获取的MRI的图像。然而，目前还没有常规的方式来表达我们对从收集的扫描仪数据重建的图像的信心。这一点很重要，因为这些图像既为研究结果提供信息，也为临床决策提供信息，而且随着人工智能重建方法的出现，对重建图像质量的不确定性的需求比以往任何时候都更大。该项目将使用最新的深入学习的生成性建模方法，并将它们直接放置到PET的成像过程中，从而生成重建图像的集合。此外，来自磁共振成像的数据将被用来为这些图像模型提供更丰富的信息。这将允许改善图像质量，虽然这本身是有益的，但反过来可能被用来减少辐射剂量，缩短扫描时间(减少运动的影响，增加患者舒适度和吞吐量)，甚至减少建立研究假设所需的受试者数量。

Aims of the PhD ProjectDevelop a PET-MR latent-space generative modelling methodology for brain PETProvide uncertainty images with the reconstructionsReduce noise and improve spatial resolution of brain PET images, to potentially lower injected radiation doses or reduce scan timePositron emission tomography (PET) is in widespread use for imaging cancer, and diseases of the heart and brain. This project concerns the case of brain imaging with a simultaneous PET-MR scanner, with potential applications in both research and clinical imaging. Brain PET imaging can be limited by noisy data and by relatively low spatial resolution, depending on the amount of radioactivity administered and the radiotracer being used.This project will use AI methodologies to make best use of additional information to help improve image quality, such as that from the simultaneously acquired MRI. However, at present, there is no routine way of expressing how confident we are in the images that are reconstructed from the collected scanner data. This matters, as these images inform both research findings as well as clinical decision making, and with the advent of AI reconstruction methods the need for uncertainty in the reconstructed image quality is greater than ever.This project will use the very latest in deep learned generative modelling methodologies and place them directly into the image formation process for PET, thus allowing ensembles of reconstructed images to be generated. Furthermore, data from MRI will be used to provide even richer information for these image models. This will allow improved image quality, which while beneficial in its own right, can in turn potentially be used to reduce radiation doses, shorten scan times (reducing impact of motion, increasing patient comfort and throughput), or even to reduce the numbers of subjects needed to establish a research hypothesis.