  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.
