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.

PHD ProjectDevelops的目的是PET-MR潜在空间生成建模方法论，用于脑PETPROVIDE不确定性图像，重建噪声并改善了脑PET图像的空间分辨率，从而在广泛地使用较低的注射辐射剂量或减少较低的注射时间PPOSITONSITRON（PET），用于广泛地使用用于对癌症和疾病的广泛使用，并在心脏和小脑中使用。该项目涉及同时使用PET-MR扫描仪的大脑成像的情况，并在研究和临床成像中使用了潜在的应用。大脑宠物成像可以受到嘈杂的数据的限制，并受到相对较低的空间分辨率的限制，具体取决于给予的放射性量和所使用的放射性练习量。该项目将使用AI方法来充分利用其他信息来帮助提高图像质量，例如同时获得的MRI。但是，目前，尚无常规方式表达我们对从收集到的扫描仪数据重建的图像中的自信心。这很重要，因为这些图像既可以为研究结果和临床决策提供信息，并且随着AI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高。该项目将使用最新的深度学习生成模型方法，并将它们直接放入宠物的图像形成过程中，从而允许重新构造的图像生成生成的图像。此外，来自MRI的数据将用于为这些图像模型提供更丰富的信息。这将允许改进的图像质量，虽然本身有益，但可能会可能用于减少辐射剂量，缩短扫描时间（减少运动的影响，增加患者舒适性和吞吐量），甚至可以减少建立研究假设所需的受试者人数。