Tactile-Guided Robotic Ultrasound: Mapping Preplanned Scan Paths for Intercostal Imaging

TL;DR

This paper presents a tactile-guided robotic ultrasound system that maps preplanned scan paths for intercostal imaging by utilizing tactile cues and robotic tracking to improve accuracy and coverage in challenging anatomical regions.

Contribution

It introduces a novel method combining tactile sensing and robotic tracking to generate accurate scan paths for intercostal ultrasound imaging, addressing limitations of traditional approaches.

Findings

Achieved mean nearest neighbor distance of 3.41 mm in scan path mapping.

Reconstructed objects beneath the bone with errors of 0.69 mm and 2.2 mm.

Validated method on four phantoms with high accuracy.

Abstract

Medical ultrasound (US) imaging is widely used in clinical examinations due to its portability, real-time capability, and radiation-free nature. To address inter- and intra-operator variability, robotic ultrasound systems have gained increasing attention. However, their application in challenging intercostal imaging remains limited due to the lack of an effective scan path generation method within the constrained acoustic window. To overcome this challenge, we explore the potential of tactile cues for characterizing subcutaneous rib structures as an alternative signal for ultrasound segmentation-free bone surface point cloud extraction. Compared to 2D US images, 1D tactile-related signals offer higher processing efficiency and are less susceptible to acoustic noise and artifacts. By leveraging robotic tracking data, a sparse tactile point cloud is generated through a few scans along the rib, mimicking human palpation. To robustly map the scanning trajectory into the intercostal space, the sparse tactile bone location point cloud is first interpolated to form a denser representation. This refined point cloud is then registered to an image-based dense bone surface point cloud, enabling accurate scan path mapping for individual patients. Additionally, to ensure full coverage of the object of interest, we introduce an automated tilt angle adjustment method to visualize structures beneath the bone. To validate the proposed method, we conducted comprehensive experiments on four distinct phantoms. The final scanning waypoint mapping achieved Mean Nearest Neighbor Distance (MNND) and Hausdorff distance (HD) errors of 3.41 mm and 3.65 mm, respectively, while the reconstructed object beneath the bone had errors of 0.69 mm and 2.2 mm compared to the CT ground truth.