Deep Kernel and Image Quality Estimators for Optimizing Robotic
  Ultrasound Controller using Bayesian Optimization
Deepak Raina SH Chandrashekhara Richard Voyles Juan Wachs Subir Kumar Saha
Ultrasound is a commonly used medical imaging modality that requires expert
sonographers to manually maneuver the ultrasound probe based on the acquired
image. Autonomous Robotic Ultrasound (A-RUS) is an appealing alternative to
this manual procedure in order to reduce sonographers' workload. The key
challenge to A-RUS is optimizing the ultrasound image quality for the region of
interest across different patients. This requires knowledge of anatomy,
recognition of error sources and precise probe position, orientation and
pressure. Sample efficiency is important while optimizing these parameters
associated with the robotized probe controller. Bayesian Optimization (BO), a
sample-efficient optimization framework, has recently been applied to optimize
the 2D motion of the probe. Nevertheless, further improvements are needed to
improve the sample efficiency for high-dimensional control of the probe. We aim
to overcome this problem by using a neural network to learn a low-dimensional
kernel in BO, termed as Deep Kernel (DK). The neural network of DK is trained
using probe and image data acquired during the procedure. The two image quality
estimators are proposed that use a deep convolution neural network and provide
real-time feedback to the BO. We validated our framework using these two
feedback functions on three urinary bladder phantoms. We obtained over 50%
increase in sample efficiency for 6D control of the robotized probe.
Furthermore, our results indicate that this performance enhancement in BO is
independent of the specific training dataset, demonstrating inter-patient
adaptability.