# RUSOpt: Robotic UltraSound Probe Normalization with Bayesian Optimization for In-plane and Out-plane Scanning

arXiv: https://arxiv.org/abs/2310.03406 · HTML: https://mlsanity.com/paper/2310.03406/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Precise Repositioning of Robotic Ultrasound: Improving Registration-based Motion Compensation using Ultrasound Confidence Optimization](https://mlsanity.com/paper/2208.05383/) (2022)
- [Robotic Sonographer: Autonomous Robotic Ultrasound using Domain Expertise in Bayesian Optimization](https://mlsanity.com/paper/2307.02442/) (2023)
- [Ultrasound Confidence Maps of Intensity and Structure Based on Directed Acyclic Graphs and Artifact Models](https://mlsanity.com/paper/2011.11956/) (2020)
- [Automated Heart and Lung Auscultation in Robotic Physical Examinations](https://mlsanity.com/paper/2201.09511/) (2022)
- [Slim U-Net: Efficient Anatomical Feature Preserving U-net Architecture for Ultrasound Image Segmentation](https://mlsanity.com/paper/2302.11524/) (2023)

## Built on by (most cited first, 2 of 2)

- [Coaching a Robotic Sonographer: Learning Robotic Ultrasound with Sparse Expert's Feedback](https://mlsanity.com/paper/2409.02337/) (2024)
- [Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms](https://mlsanity.com/paper/2503.06795/) (2025)

## Abstract (arXiv)

The one of the significant challenges faced by autonomous robotic ultrasound systems is acquiring high-quality images across different patients. The proper orientation of the robotized probe plays a crucial role in governing the quality of ultrasound images. To address this challenge, we propose a sample-efficient method to automatically adjust the orientation of the ultrasound probe normal to the point of contact on the scanning surface, thereby improving the acoustic coupling of the probe and resulting image quality. Our method utilizes Bayesian Optimization (BO) based search on the scanning surface to efficiently search for the normalized probe orientation. We formulate a novel objective function for BO that leverages the contact force measurements and underlying mechanics to identify the normal. We further incorporate a regularization scheme in BO to handle the noisy objective function. The performance of the proposed strategy has been assessed through experiments on urinary bladder phantoms. These phantoms included planar, tilted, and rough surfaces, and were examined using both linear and convex probes with varying search space limits. Further, simulation-based studies have been carried out using 3D human mesh models. The results demonstrate that the mean (±SD) absolute angular error averaged over all phantoms and 3D models is boldsymbol2.4±0.7^circ and boldsymbol2.1±1.3^circ, respectively.