Synthetic ultrasound images to benchmark echocardiography-based biomechanics

TL;DR

This paper introduces a method to generate synthetic ultrasound images from finite element cardiac simulations, providing benchmark data to improve the reproducibility of motion estimation algorithms in echocardiography.

Contribution

The study presents a novel approach to produce synthetic B-mode ultrasound images from FE simulations, enabling standardized benchmarking of cardiac motion analysis methods.

Findings

Synthetic images qualitatively match FE displacement patterns

Provides benchmark quantities for motion estimation algorithms

Facilitates reproducibility in cardiac ultrasound analysis

Abstract

Brightness mode (B-mode) ultrasound is a common imaging modality in the clinical assessment of several cardiovascular diseases. The utility of ultrasound-based functional indices such as the ejection fraction (EF) and stroke volume (SV) is widely described in diagnosing advanced-stage cardiovascular diseases. Additionally, structural indices obtained through the analysis of cardiac motion have been found to be important in the early-stage assessment of structural heart diseases, such as hypertrophic cardiomyopathy and myocardial infarction. Estimating heterogeneous variations in cardiac motion through B-mode ultrasound imaging is a crucial component of patient care. Despite the benefits of such imaging techniques, motion estimation algorithms are susceptible to variability between vendors due to the lack of benchmark motion quantities. In contrast, finite element (FE) simulations of cardiac biomechanics leverage well-established constitutive models of the myocardium to ensure reproducibility. In this study, we developed a methodology to create synthetic B-mode ultrasound images from FE simulations. The proposed methodology provides a detailed representation of displacements and strains under complex mouse-specific loading protocols of the LV. A comparison between the synthetic images and FE simulations revealed qualitative similarity in displacement patterns, thereby yielding benchmark quantities to improve the reproducibility of motion estimation algorithms. Thus, the study provides a methodology to create an extensive repository of images describing complex motion patterns to facilitate the enhanced reproducibility of cardiac motion analysis.