Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction

TL;DR

This paper introduces ReMamba, a novel approach leveraging multi-directional state space models and multi-modal data to enhance fine-grained 3D ultrasound reconstruction, significantly outperforming existing methods.

Contribution

The paper presents a multi-scale spatio-temporal learning framework with adaptive fusion and online alignment strategies for improved freehand 3D ultrasound reconstruction.

Findings

Significant performance improvements over existing methods.

Effective multi-modal fusion with inertial measurement units.

Enhanced long-range dependency modeling with state space models.

Abstract

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.