# MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction

arXiv: https://arxiv.org/abs/2506.15835 · HTML: https://mlsanity.com/paper/2506.15835/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 60 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [FILM: Frame Interpolation for Large Motion](https://mlsanity.com/paper/2202.04901/) (2022)
- [A Survey of Visual Transformers](https://mlsanity.com/paper/2111.06091/) (2021)
- [MobileNetV4 -- Universal Models for the Mobile Ecosystem](https://mlsanity.com/paper/2404.10518/) (2024)
- [Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images](https://mlsanity.com/paper/2403.12570/) (2024)
- [Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis](https://mlsanity.com/paper/2007.07222/) (2020)
- [Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis](https://mlsanity.com/paper/2204.06929/) (2022)
- [Why ResNet Works? Residuals Generalize](https://mlsanity.com/paper/1904.01367/) (2019)
- [Attention Guided Network for Retinal Image Segmentation](https://mlsanity.com/paper/1907.12930/) (2019)
- [ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation](https://mlsanity.com/paper/2109.12108/) (2021)
- [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)
- [Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning](https://mlsanity.com/paper/2006.07694/) (2020)
- [Context Matters: Graph-based Self-supervised Representation Learning for Medical Images](https://mlsanity.com/paper/2012.06457/) (2020)
- [Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames](https://mlsanity.com/paper/2211.04867/) (2022)
- [Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker](https://mlsanity.com/paper/2310.10248/) (2023)
- [Multi-IMU with Online Self-Consistency for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2306.16197/) (2023)

## Built on by (most cited first, 3 of 3)

- [TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker](https://mlsanity.com/paper/2506.21765/) (2025)
- [MLRecon: Robust Markerless Freehand 3D Ultrasound Reconstruction via Coarse-to-Fine Pose Estimation](https://mlsanity.com/paper/2603.00990/) (2026)
- [Seeing Globally, Refining Locally: Global Visual Guidance and Local Ultrasound Cues for Robust Freehand 3-D Ultrasound Reconstruction](https://mlsanity.com/paper/2607.12398/) (2026)

## Abstract (arXiv)

Three-dimensional (3D) ultrasound (US) aims to provide sonographers with the spatial relationships of anatomical structures, playing a crucial role in clinical diagnosis. Recently, deep-learning-based freehand 3D US has made significant advancements. It reconstructs volumes by estimating transformations between images without external tracking. However, image-only reconstruction poses difficulties in reducing cumulative drift and further improving reconstruction accuracy, particularly in scenarios involving complex motion trajectories. In this context, we propose an enhanced motion network (MoNetV2) to enhance the accuracy and generalizability of reconstruction under diverse scanning velocities and tactics. First, we propose a sensor-based temporal and multi-branch structure that fuses image and motion information from a velocity perspective to improve image-only reconstruction accuracy. Second, we devise an online multi-level consistency constraint that exploits the inherent consistency of scans to handle various scanning velocities and tactics. This constraint exploits both scan-level velocity consistency, path-level appearance consistency, and patch-level motion consistency to supervise inter-frame transformation estimation. Third, we distill an online multi-modal self-supervised strategy that leverages the correlation between network estimation and motion information to further reduce cumulative errors. Extensive experiments clearly demonstrate that MoNetV2 surpasses existing methods in both reconstruction quality and generalizability performance across three large datasets.