Abstract
Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used in multimodal analysis of neurodegenerative disorders. While MRI is broadly utilized in clinical settings, PET is less accessible. Many studies have attempted to use deep generative models to synthesize PET from MRI scans. However, they often suffer from unstable training and inadequately preserve brain functional information conveyed by PET. To this end, we propose a functional imaging constrained diffusion (FICD) framework for 3D brain PET image synthesis with paired structural MRI as input condition, through a new constrained diffusion model (CDM). The FICD introduces noise to PET and then progressively removes it with CDM, ensuring high output fidelity throughout a stable training phase. The CDM learns to predict denoised PET with a functional imaging constraint introduced to ensure voxel-wise alignment between each denoised PET and its ground truth. Quantitative and qualitative analyses conducted on 293 subjects with paired T1-weighted MRI and 18F-fluorodeoxyglucose (FDG)-PET scans suggest that FICD achieves superior performance in generating FDG-PET data compared to state-of-the-art methods. We further validate the effectiveness of the proposed FICD on data from a total of 1,262 subjects through three downstream tasks, with experimental results suggesting its utility and generalizability.
Abstract (translated)
磁共振成像（MRI）和正电子发射断层扫描（PET）在多模态分析神经退行性疾病方面越来越受到欢迎。尽管MRI在临床环境中得到了广泛应用，但PET却不太易获取。许多研究试图使用深度生成模型从MRI扫描中合成PET，但这些模型往往在训练过程中不稳定，并且不能很好地保留PET中传递给大脑的功能信息。为此，我们提出了一个功能成像约束扩散（FICD）框架，用于使用成对结构MRI生成3D脑PET图像，并通过一个新的约束扩散模型（CDM）实现。FICD引入了噪声到PET，然后通过CDM逐步去除它，确保在稳定的训练阶段具有高输出保真度。CDM学会了通过引入功能成像约束来预测去噪PET，以确保每个去噪PET与其实际对照之间进行逐个像素对齐。对293名成对T1加权MRI和18F-氟代葡萄糖（FDG）-PET扫描的受试者的定量定性分析结果表明，FICD在生成FDG-PET数据方面具有比现有方法更卓越的性能。我们还通过三个下游任务验证了所提出的FICD在总1262个受试者数据上的有效性，实验结果表明了其效用和可扩展性。
URL
https://arxiv.org/abs/2405.02504
