Fully Bayesian 3D PET-MR Neuroimaging Reconstruction

完全贝叶斯 3D PET-MR 神经影像重建

• 批准号: 2886403
• 金额: --
• 依托单位: King's 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-2886403
• 关键词: Fully; Bayesian; 3D; PET; MR

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.

博士项目的目标为脑PET开发一种PET- mr潜在空间生成建模方法通过重建提供不确定性图像降低噪声并提高脑PET图像的空间分辨率，以潜在地降低注射辐射剂量或缩短扫描时间正电子发射断层扫描（PET）广泛用于成像癌症，心脏和脑部疾病。该项目涉及同时使用PET-MR扫描仪进行脑成像的情况，在研究和临床成像中都有潜在的应用。脑PET成像可能受到噪声数据和相对较低的空间分辨率的限制，这取决于所施用的放射性量和所使用的放射性示踪剂。该项目将使用人工智能方法来充分利用额外的信息来帮助提高图像质量，例如同时获取的MRI。然而，目前还没有常规的方法来表达我们对从采集到的扫描仪数据重建的图像有多自信。这很重要，因为这些图像为研究结果和临床决策提供了信息，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要大。该项目将使用最新的深度学习生成建模方法，并将它们直接放入PET的图像形成过程中，从而允许生成重建图像的集合。此外，来自MRI的数据将为这些图像模型提供更丰富的信息。这将提高图像质量，而这本身是有益的，可以反过来潜在地用于减少辐射剂量，缩短扫描时间（减少运动的影响，增加患者的舒适度和吞吐量），甚至减少建立研究假设所需的受试者数量。