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.

翻译：超声成像因其安全性、可重复性和实时性，已成为临床诊断中的常用工具。自由手3D超声技术可在不增加复杂性的前提下提供对扫描区域的更深层理解。然而，仰角位移估计与累积误差仍是难题，导致仅凭图像难以推断相对位置。已有研究提出添加外部轻量级传感器以在不增加复杂度的前提下提升重建性能，该方法已被证实具有有效性。我们提出一种基于多惯性测量单元的新型在线自一致性网络（OSCNet），旨在提升重建性能。OSCNet采用模态级自监督策略融合多IMU信息，并减少各IMU数据所得重建结果之间的差异。此外，提出序列级自一致性策略以增强扫描序列及其子序列预测结果的层次一致性。在采用多种扫描策略的大规模手臂和颈动脉数据集上的实验表明，我们的OSCNet优于以往方法，实现了最先进的的重建性能。