Geometry adaptive waveformer for cardio-vascular modeling
This work addresses the problem of computationally expensive numerical simulations for cardiovascular modeling, offering a machine learning solution that could benefit clinical applications, though it appears incremental in its approach.
The paper tackled the challenge of modeling cardiovascular anatomies by proposing a geometry adaptive waveformer model to predict blood flow dynamics, achieving results that address computational efficiency and accuracy in clinical settings.
Modeling cardiovascular anatomies poses a significant challenge due to their complex, irregular structures and inherent pathological conditions. Numerical simulations, while accurate, are often computationally expensive, limiting their practicality in clinical settings. Traditional machine learning methods, on the other hand, often struggle with some major hurdles, including high dimensionality of the inputs, inability to effectively work with irregular grids, and preserving the time dependencies of responses in dynamic problems. In response to these challenges, we propose a geometry adaptive waveformer model to predict blood flow dynamics in the cardiovascular system. The framework is primarily composed of three components: a geometry encoder, a geometry decoder, and a waveformer. The encoder transforms input defined on the irregular domain to a regular domain using a graph operator-based network and signed distance functions. The waveformer operates on the transformed field on the irregular grid. Finally, the decoder reverses this process, transforming the output from the regular grid back to the physical space. We evaluate the efficacy of the approach on different sets of cardiovascular data.