Multi-IMU with Online Self-Consistency for Freehand 3D Ultrasound Reconstruction

TL;DR

This paper introduces OSCNet, an online self-consistency network utilizing multiple IMUs and self-supervised strategies to enhance freehand 3D ultrasound reconstruction accuracy and robustness.

Contribution

The paper presents a novel multi-IMU online self-consistency network with self-supervised fusion strategies for improved 3D ultrasound reconstruction.

Findings

Outperforms previous methods on large-scale datasets

Achieves state-of-the-art reconstruction accuracy

Demonstrates robustness across different scanning tactics

Abstract

Ultrasound (US) imaging is a popular tool in clinical diagnosis, offering safety, repeatability, and real-time capabilities. Freehand 3D US is a technique that provides a deeper understanding of scanned regions without increasing complexity. However, estimating elevation displacement and accumulation error remains challenging, making it difficult to infer the relative position using images alone. The addition of external lightweight sensors has been proposed to enhance reconstruction performance without adding complexity, which has been shown to be beneficial. We propose a novel online self-consistency network (OSCNet) using multiple inertial measurement units (IMUs) to improve reconstruction performance. OSCNet utilizes a modal-level self-supervised strategy to fuse multiple IMU information and reduce differences between reconstruction results obtained from each IMU data. Additionally, a sequence-level self-consistency strategy is proposed to improve the hierarchical consistency of prediction results among the scanning sequence and its sub-sequences. Experiments on large-scale arm and carotid datasets with multiple scanning tactics demonstrate that our OSCNet outperforms previous methods, achieving state-of-the-art reconstruction performance.