Brain structural MRI has been widely used to assess the future progression of cognitive impairment (CI). Previous learning-based studies usually suffer from the issue of small-sized labeled training data, while there exist a huge amount of structural MRIs in large-scale public databases. Intuitively, brain anatomical structures derived from these public MRIs (even without task-specific label information) can be used to boost CI progression trajectory prediction. However, previous studies seldom take advantage of such brain anatomy prior. To this end, this paper proposes a brain anatomy prior modeling (BAPM) framework to forecast the clinical progression of cognitive impairment with small-sized target MRIs by exploring anatomical brain structures. Specifically, the BAPM consists of a pretext model and a downstream model, with a shared brain anatomy-guided encoder to model brain anatomy prior explicitly. Besides the encoder, the pretext model also contains two decoders for two auxiliary tasks (i.e., MRI reconstruction and brain tissue segmentation), while the downstream model relies on a predictor for classification. The brain anatomy-guided encoder is pre-trained with the pretext model on 9,344 auxiliary MRIs without diagnostic labels for anatomy prior modeling. With this encoder frozen, the downstream model is then fine-tuned on limited target MRIs for prediction. We validate the BAPM on two CI-related studies with T1-weighted MRIs from 448 subjects. Experimental results suggest the effectiveness of BAPM in (1) four CI progression prediction tasks, (2) MR image reconstruction, and (3) brain tissue segmentation, compared with several state-of-the-art methods.

翻译：脑部结构磁共振成像（MRI）已被广泛用于评估认知障碍（CI）的未来进展。以往基于学习的研究通常面临标注训练数据规模小的问题，而大规模公共数据库中存有海量结构MRI数据。直观而言，从这些公共MRI（即使不含任务特定标注信息）中提取的脑解剖结构可用于提升CI进展轨迹预测能力，但现有研究鲜少利用此类脑解剖先验知识。为此，本文提出一种脑解剖先验建模（BAPM）框架，通过探索脑部解剖结构，利用小规模目标MRI预测认知障碍临床进展。具体而言，BAPM由预训练模型和下游模型组成，共享一个脑解剖引导编码器以显式建模脑解剖先验。除编码器外，预训练模型还包含两个用于辅助任务（MRI重建与脑组织分割）的解码器，而下游模型则依赖分类预测器。该脑解剖引导编码器基于9,344例无诊断标注的辅助MRI通过预训练模型进行解剖先验建模。冻结编码器参数后，下游模型在有限的目标MRI上进行微调以实现预测。我们在两项CI相关研究中采用448名受试者的T1加权MRI验证BAPM。实验结果表明，与多种前沿方法相比，BAPM在以下三方面均具有效性：（1）四项CI进展预测任务；（2）MR图像重建；（3）脑组织分割。