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（US）成像因其安全性、可重复性和实时性成为临床诊断中的常用工具。自由手部三维超声技术能够在不增加操作复杂度的前提下提供扫描区域更深入的理解。然而，估计仰角位移和累积误差仍然具有挑战性，这使得仅通过图像推断相对位置十分困难。为提升重建性能而不增加操作复杂性，有研究提出添加外部轻量级传感器，并已证实其有效性。本文提出一种新颖的在线自一致性网络（OSCNet），利用多个惯性测量单元（IMU）来改善重建性能。OSCNet采用模态级自监督策略融合多IMU信息，并减小各IMU数据所得重建结果之间的差异。此外，还提出序列级自一致性策略，以提升扫描序列及其子序列预测结果的分层一致性。在多种扫描策略下的大规模手臂和颈动脉数据集上的实验表明，我们的OSCNet优于先前方法，取得了最先进的重建性能。