In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs. Existing deep generative models developed for this task are primarily data-driven and face challenges in learning disease progressions. BrLP addresses these challenges by incorporating prior knowledge from disease models to enhance the accuracy of predictions. To implement this, we propose to integrate an auxiliary model that infers volumetric changes in various brain regions. Additionally, we introduce Latent Average Stabilization (LAS), a novel technique to improve spatiotemporal consistency of the predicted progression. BrLP is trained and evaluated on a large dataset comprising 11,730 T1-weighted brain MRIs from 2,805 subjects, collected from three publicly available, longitudinal Alzheimer's Disease (AD) studies. In our experiments, we compare the MRI scans generated by BrLP with the actual follow-up MRIs available from the subjects, in both cross-sectional and longitudinal settings. BrLP demonstrates significant improvements over existing methods, with an increase of 22% in volumetric accuracy across AD-related brain regions and 43% in image similarity to the ground-truth scans. The ability of BrLP to generate conditioned 3D scans at the subject level, along with the novelty of integrating prior knowledge to enhance accuracy, represents a significant advancement in disease progression modeling, opening new avenues for precision medicine. The code of BrLP is available at the following link: https://github.com/LemuelPuglisi/BrLP.

翻译：本研究提出脑部潜在进展模型（BrLP），一种基于潜在扩散的新型时空疾病进展模型。BrLP旨在通过三维脑部MRI数据，在个体层面预测疾病的演化过程。现有针对该任务开发的深度生成模型主要依赖数据驱动，在疾病进展学习方面面临挑战。BrLP通过整合疾病模型的先验知识来提升预测准确性，从而解决这些挑战。为实现该目标，我们提出集成一个辅助模型，用于推断不同脑区的体积变化。此外，我们引入潜在平均稳定化（LAS）技术，这是一种新型技术，旨在改善预测进展的时空一致性。BrLP在包含来自2,805名受试者的11,730张T1加权脑部MRI的大规模数据集上进行训练和评估，这些数据收集自三项公开的纵向阿尔茨海默病（AD）研究。在实验中，我们在横断面和纵向两种情境下，将BrLP生成的MRI扫描与实际随访MRI进行对比。与现有方法相比，BrLP表现出显著提升，在AD相关脑区的体积精度上提高22%，在图像与实际扫描的相似性上提高43%。BrLP在个体层面生成条件化三维扫描的能力，以及整合先验知识以提升准确性的创新性，代表了疾病进展建模领域的重大进步，为精准医学开辟了新途径。BrLP代码可在以下链接获取：https://github.com/LemuelPuglisi/BrLP。