GReAT: leveraging geometric artery data to improve wall shear stress assessment
This work addresses data scarcity in cardiovascular health for improving hemodynamic biomarker assessment, but it is incremental as it applies existing self-supervised methods to a new domain.
The authors tackled the challenge of limited data for training machine learning models to assess wall shear stress in coronary arteries by using a large dataset of 3D geometric artery models for self-supervised pre-training, showing that this approach improved segmentation into stress regions even with only 49 patient samples.
Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed from patient-specific medical images via machine learning algorithms, bypassing the need for time-intensive computational fluid simulation. However, it is extremely challenging to amass large-enough datasets to effectively train such models. We could address this data scarcity by means of self-supervised pre-training and foundations models given large datasets of geometric artery models. In the context of coronary arteries, leveraging learned representations to improve hemodynamic biomarker assessment has not yet been well studied. In this work, we address this gap by investigating whether a large dataset (8449 shapes) consisting of geometric models of 3D blood vessels can benefit wall shear stress assessment in coronary artery models from a small-scale clinical trial (49 patients). We create a self-supervised target for the 3D blood vessels by computing the heat kernel signature, a quantity obtained via Laplacian eigenvectors, which captures the very essence of the shapes. We show how geometric representations learned from this datasets can boost segmentation of coronary arteries into regions of low, mid and high (time-averaged) wall shear stress even when trained on limited data.