Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach
This work addresses the need for improved statistical shape modeling in clinical investigations of organ cycles or disease progression over time, particularly for atrial-fibrillation patients, though it is incremental as it builds on existing particle-based shape modeling methods.
The paper tackles the problem of modeling spatiotemporal shape changes in cardiac anatomy by proposing a data-driven approach that learns population-level shape variations directly from shape data, demonstrating its efficacy on 4D cardiac data from atrial-fibrillation patients and showing it outperforms an image-based method with better generalization and specificity in a Linear Dynamical System model.
Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM). Such a tool enables characterizing patient organ cycles or disease progression in relation to a cohort of interest. Constructing shape models requires establishing a quantitative shape representation (e.g., corresponding landmarks). Particle-based shape modeling (PSM) is a data-driven SSM approach that captures population-level shape variations by optimizing landmark placement. However, it assumes cross-sectional study designs and hence has limited statistical power in representing shape changes over time. Existing methods for modeling spatiotemporal or longitudinal shape changes require predefined shape atlases and pre-built shape models that are typically constructed cross-sectionally. This paper proposes a data-driven approach inspired by the PSM method to learn population-level spatiotemporal shape changes directly from shape data. We introduce a novel SSM optimization scheme that produces landmarks that are in correspondence both across the population (inter-subject) and across time-series (intra-subject). We apply the proposed method to 4D cardiac data from atrial-fibrillation patients and demonstrate its efficacy in representing the dynamic change of the left atrium. Furthermore, we show that our method outperforms an image-based approach for spatiotemporal SSM with respect to a generative time-series model, the Linear Dynamical System (LDS). LDS fit using a spatiotemporal shape model optimized via our approach provides better generalization and specificity, indicating it accurately captures the underlying time-dependency.