Yuan Chenxi, Duan Jinhao, Xu Kaidi, Tustison Nicholas J, Hubbard Rebecca A, Linn Kristin A
Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Imaging Neurosci (Camb). 2024 Oct 22;2. doi: 10.1162/imag_a_00323. eCollection 2024.
Missing data is a significant challenge in medical research. In longitudinal studies of Alzheimer's disease (AD) where structural magnetic resonance imaging (MRI) is collected from individuals at multiple time points, participants may miss a study visit or drop out. Additionally, technical issues such as participant motion in the scanner may result in unusable imaging data at designated visits. Such missing data may hinder the development of high-quality imaging-based biomarkers. To address the problem of missing MRI data in studies of AD, we introduced a novel 3D diffusion model specifically designed for imputing missing structural MRI (Recovery of Missing Neuroimaging using Diffusion models (ReMiND)). The model generates a whole-brain image conditional on a single structural MRI observed at a past visit or conditional on one past and one future observed structural MRI relative to the missing observation. The performance of models was compared with two alternative imputation approaches: forward filling and image generation using variational autoencoders. Experimental results show that our method can generate 3D structural MRI with high similarity to ground-truth images at designated visits. Furthermore, images generated using ReMiND show relatively lower differences in volume estimation between the imputed and observed images compared to images generated by forward filling or autoencoders. Additionally, ReMiND provides more accurate estimated rates of atrophy over time in important anatomical brain regions than the two comparator methods. Our 3D diffusion model can impute missing structural MRI data at a single designated visit and outperforms alternative methods for imputing whole-brain images that are missing from longitudinal trajectories.
缺失数据是医学研究中的一项重大挑战。在阿尔茨海默病（AD）的纵向研究中，需要在多个时间点收集个体的结构磁共振成像（MRI），参与者可能会错过研究访视或退出研究。此外，诸如参与者在扫描仪中的移动等技术问题可能导致在指定访视时产生无法使用的成像数据。此类缺失数据可能会阻碍高质量基于成像的生物标志物的开发。为了解决AD研究中MRI数据缺失的问题，我们引入了一种专门设计用于插补缺失结构MRI的新型3D扩散模型（使用扩散模型恢复缺失神经成像（ReMiND））。该模型基于在过去访视时观察到的单个结构MRI，或基于相对于缺失观察的一个过去和一个未来观察到的结构MRI生成全脑图像。将模型的性能与两种替代插补方法进行了比较：向前填充和使用变分自编码器进行图像生成。实验结果表明，我们的方法可以在指定访视时生成与真实图像高度相似的3D结构MRI。此外，与通过向前填充或自编码器生成的图像相比，使用ReMiND生成的图像在插补图像和观察图像之间的体积估计差异相对较小。此外，与两种比较方法相比，ReMiND在重要的大脑解剖区域随时间提供了更准确的萎缩估计率。我们的3D扩散模型可以在单个指定访视时插补缺失的结构MRI数据，并且在插补纵向轨迹中缺失的全脑图像方面优于替代方法。
