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

arXiv: https://arxiv.org/abs/2407.04242 · HTML: https://mlsanity.com/paper/2407.04242/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2108.00274/) (2021)
- [Deep Motion Network for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2207.00177/) (2022)
- [Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames](https://mlsanity.com/paper/2211.04867/) (2022)
- [Multi-IMU with Online Self-Consistency for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2306.16197/) (2023)

## Built on by (most cited first, 5 of 5)

- [EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance](https://mlsanity.com/paper/2504.13065/) (2025)
- [TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker](https://mlsanity.com/paper/2506.21765/) (2025)
- [Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using Deep Learning](https://mlsanity.com/paper/2502.03505/) (2025)
- [UltrasODM: A Dual Stream Optical Flow Mamba Network for 3D Freehand Ultrasound Reconstruction](https://mlsanity.com/paper/2512.07756/) (2025)
- [MLRecon: Robust Markerless Freehand 3D Ultrasound Reconstruction via Coarse-to-Fine Pose Estimation](https://mlsanity.com/paper/2603.00990/) (2026)

## Abstract (arXiv)

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.