A Curvature and Trajectory Optimization-based 3D Surface Reconstruction Pipeline for Ultrasound Trajectory Generation

Ultrasound scanning is an efficient imaging modality preferred for quick medical procedures. However, due to the lack of skilled sonographers, researchers have developed many Robotic Ultrasound System (RUS) prototypes for various procedures. Most of these systems have a human-in-the-loop and require an expert to point the robot to the region of the subject to be scanned. Only a few systems try to incorporate some knowledge from the exterior shape of the subject for ultrasound scanning. Accurate 3D surface reconstruction of a patient's exterior can enable an RUS to perceive subjects more like a clinician would. It can help localize the subject for the robot while eliminating input from an expert. Ultrasound scanning trajectories can be better planned if the RUS first detects critical regions on the surface of the subject and corresponding curvatures. We use an RGB-D sensor to acquire point clouds representing the 3D surface of the subject, which in the present work is for a lower-torso leg phantom. A consolidated pipeline for creating an optimized 3D surface reconstruction of a subject is presented and is used to autonomously identify a region of interest for scanning femoral vessels with an ultrasound probe. To make our system more robust to inter-subject variations in shape and size, we incorporate a trajectory optimization module of the RUS-mounted RGB-D sensor. To this end, we introduce a comprehensive evaluation score to quantify the quality of point cloud reconstructions. The resulting improvements in 3D surface scanning and reconstruction enable near-automation in generating ultrasound scanning trajectories for femoral vessels. Our pipeline produces ultrasound images with an average ZNCC score of 0.86 and our 3D point cloud reconstructions are accurate up to 1e-5 m from a ground-truth high-resolution CT scan.