Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. This model is trained and validated using 3D structural magnetic resonance imaging data from the ADNI cohort. Results show that the framework can estimate realistic deformations, following the known course of Alzheimer's disease, that clearly differentiate between healthy and demented patterns of ageing. This suggests the framework has potential to be incorporated into explainable models of disease, for the exploration of interventions and counterfactual examples.
翻译:组织变形的生物机能模型可用于模拟纵向大脑进化的不同情景。在这项工作中,我们为大脑萎缩、健康老化和阿尔茨海默氏病的超弹性菌株建模提供了一个深层次学习框架。该框架直接模拟了年龄、疾病状况和扫描间隔的影响,以扭转区域萎缩模式,从中得出基于菌种模型的变形估计。这一模型使用ADNI组群提供的3D结构磁共振成像数据进行培训和验证。结果显示,该框架可以估计出现实的变形,并遵循已知的阿尔茨海默氏病趋势,明确区分健康的老龄化模式和痴呆的老龄化模式。这说明,该框架有可能纳入可解释的疾病模型,用于探索干预措施和反实际例子。