RUSOpt: Robotic UltraSound Probe Normalization with Bayesian
  Optimization for In-plane and Out-plane Scanning
Deepak Raina Abhishek Mathur Richard M. Voyles Juan Wachs SH Chandrashekhara Subir Kumar Saha
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 ($\pm$SD) absolute angular error averaged over all phantoms and 3D models
is $\boldsymbol{2.4\pm0.7^\circ}$ and $\boldsymbol{2.1\pm1.3^\circ}$,
respectively.