  Fine-grained spatio-temporal learning is crucial for freehand 3D ultrasound
reconstruction. Previous works mainly resorted to the coarse-grained spatial
features and the separated temporal dependency learning and struggles for
fine-grained spatio-temporal learning. Mining spatio-temporal information in
fine-grained scales is extremely challenging due to learning difficulties in
long-range dependencies. In this context, we propose a novel method to exploit
the long-range dependency management capabilities of the state space model
(SSM) to address the above challenge. Our contribution is three-fold. First, we
propose ReMamba, which mines multi-scale spatio-temporal information by
devising a multi-directional SSM. Second, we propose an adaptive fusion
strategy that introduces multiple inertial measurement units as auxiliary
temporal information to enhance spatio-temporal perception. Last, we design an
online alignment strategy that encodes the temporal information as pseudo
labels for multi-modal alignment to further improve reconstruction performance.
Extensive experimental validations on two large-scale datasets show remarkable
improvement from our method over competitors.
