Follow Your Heart: Landmark-Guided Transducer Pose Scoring for Point-of-Care Echocardiography

TL;DR

This paper introduces a multi-task neural network that guides novice users in acquiring high-quality apical 4-chamber views in point-of-care echocardiography and estimates left ventricular ejection fraction without requiring transducer tracking.

Contribution

It presents a pose scoring and landmark detection network that provides real-time feedback and automatic LVEF estimation using only image data, simplifying deployment in resource-limited settings.

Findings

The network accurately determines transducer pose quality from images alone.

It provides visual cues to guide anatomical orientation during image acquisition.

The approach enables effective LVEF estimation without costly tracking equipment.

Abstract

Point-of-care transthoracic echocardiography (TTE) makes it possible to assess a patient's cardiac function in almost any setting. A critical step in the TTE exam is acquisition of the apical 4-chamber (A4CH) view, which is used to evaluate clinically impactful measurements such as left ventricular ejection fraction (LVEF). However, optimizing transducer pose for high-quality image acquisition and subsequent measurement is a challenging task, particularly for novice users. In this work, we present a multi-task network that provides feedback cues for A4CH view acquisition and automatically estimates LVEF in high-quality A4CH images. The network cascades a transducer pose scoring module and an uncertainty-aware LV landmark detector with automated LVEF estimation. A strength is that network training and inference do not require cumbersome or costly setups for transducer position tracking. We evaluate performance on point-of-care TTE data acquired with a spatially dense "sweep" protocol around the optimal A4CH view. The results demonstrate the network's ability to determine when the transducer pose is on target, close to target, or far from target based on the images alone, while generating visual landmark cues that guide anatomical interpretation and orientation. In conclusion, we demonstrate a promising strategy to provide guidance for A4CH view acquisition, which may be useful when deploying point-of-care TTE in limited resource settings.