# Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph

arXiv: https://arxiv.org/abs/2307.03800 · HTML: https://mlsanity.com/paper/2307.03800/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study](https://mlsanity.com/paper/1607.08371/) (2016)
- [Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback](https://mlsanity.com/paper/2011.00099/) (2020)
- [Deformation-Aware Robotic 3D Ultrasound](https://mlsanity.com/paper/2107.08411/) (2021)
- [Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion](https://mlsanity.com/paper/2208.05399/) (2022)
- [Skeleton Graph-based Ultrasound-CT Non-rigid Registration](https://mlsanity.com/paper/2305.08228/) (2023)

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

- [DefCor-Net: Physics-Aware Ultrasound Deformation Correction](https://mlsanity.com/paper/2308.03865/) (2023)
- [Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging](https://mlsanity.com/paper/2406.04100/) (2024)
- [Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning](https://mlsanity.com/paper/2404.09927/) (2024)

## Abstract (arXiv)

Autonomous ultrasound (US) imaging has gained increased interest recently, and it has been seen as a potential solution to overcome the limitations of free-hand US examinations, such as inter-operator variations. However, it is still challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications with high acoustic-impedance bone structures under the skin. To address this challenge, a graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup by explicitly considering subcutaneous bone surface features instead of the skin surface. To this end, the sternum and cartilage branches are segmented using a template matching to assist coarse alignment of US and CT point clouds. Afterward, a directed graph is generated based on the CT template. Then, the self-organizing map using geographical distance is successively performed twice to extract the optimal graph representations for CT and US point clouds, individually. To evaluate the proposed approach, five cartilage point clouds from distinct patients are employed. The results demonstrate that the proposed graph-based registration can effectively map trajectories from CT to the current setup for displaying US views through limited intercostal space. The non-rigid registration results in terms of Hausdorff distance (Mean±SD) is 9.48±0.27 mm and the path transferring error in terms of Euclidean distance is 2.21±1.11 mm.