Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can often be addressed by image segmentation and motion tracking algorithms, our capability to model and to answer the second question is still limited. In this work, we propose a novel conditional generative model to describe the 4D spatio-temporal anatomy of the heart and its interaction with non-imaging clinical factors. The clinical factors are integrated as the conditions of the generative modelling, which allows us to investigate how these factors influence the cardiac anatomy. We evaluate the model performance in mainly two tasks, anatomical sequence completion and sequence generation. The model achieves a high performance in anatomical sequence completion, comparable to or outperforming other state-of-the-art generative models. In terms of sequence generation, given clinical conditions, the model can generate realistic synthetic 4D sequential anatomies that share similar distributions with the real data.

翻译：心脏图像分析的两个关键问题是从图像中评估心脏的解剖结构和运动，以及理解它们如何与性别、年龄和疾病等非影像临床因素相关联。尽管第一个问题通常可以通过图像分割和运动追踪算法解决，但我们建模并回答第二个问题的能力仍然有限。在这项工作中，我们提出了一种新颖的条件生成模型，用以描述心脏的四维时空解剖结构及其与非影像临床因素的相互作用。临床因素被整合为生成模型的条件，这使我们能够研究这些因素如何影响心脏解剖结构。我们主要从解剖序列补全和序列生成两个任务评估模型性能。该模型在解剖序列补全任务中表现出色，性能可与现有最优生成模型相媲美或更优。在序列生成方面，给定临床条件，该模型能够生成与真实数据分布相似的真实合成四维序列解剖结构。