# Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework

arXiv: https://arxiv.org/abs/2111.02167 · HTML: https://mlsanity.com/paper/2111.02167/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study](https://mlsanity.com/paper/1607.08371/) (2016)
- [Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound](https://mlsanity.com/paper/2007.04480/) (2020)
- [Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.00718/) (2021)
- [Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1806.03228/) (2018)
- [Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.13321/) (2020)
- [Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network](https://mlsanity.com/paper/1806.07486/) (2018)
- [Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/1910.04331/) (2019)
- [Straight to the point: reinforcement learning for user guidance in ultrasound](https://mlsanity.com/paper/1903.00586/) (2019)

## Built on by (most cited first, 6 of 6)

- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)
- [Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2207.00475/) (2022)
- [Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning](https://mlsanity.com/paper/2404.09927/) (2024)
- [US-X Complete: A Multi-Modal Approach to Anatomical 3D Shape Recovery](https://mlsanity.com/paper/2511.15600/) (2025)
- [Shape Completion and Real-Time Visualization in Robotic Ultrasound Spine Acquisitions](https://mlsanity.com/paper/2508.08923/) (2025)
- [Deep Kernel and Image Quality Estimators for Optimizing Robotic Ultrasound Controller using Bayesian Optimization](https://mlsanity.com/paper/2310.07392/) (2023)

## Abstract (arXiv)

Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers. In this work, we propose a novel dual-agent framework that integrates a reinforcement learning (RL) agent and a deep learning (DL) agent to jointly determine the movement of the US probe based on the real-time US images, in order to mimic the decision-making process of an expert sonographer to achieve autonomous standard view acquisitions in spinal sonography. Moreover, inspired by the nature of US propagation and the characteristics of the spinal anatomy, we introduce a view-specific acoustic shadow reward to utilize the shadow information to implicitly guide the navigation of the probe toward different standard views of the spine. Our method is validated in both quantitative and qualitative experiments in a simulation environment built with US data acquired from 17 volunteers. The average navigation accuracy toward different standard views achieves 5.18mm/5.25deg and 12.87mm/17.49deg in the intra- and inter-subject settings, respectively. The results demonstrate that our method can effectively interpret the US images and navigate the probe to acquire multiple standard views of the spine.