Unsupervised Deformable Image Registration for Respiratory Motion
  Compensation in Ultrasound Images
FNU Abhimanyu Andrew L. Orekhov John Galeotti Howie Choset
In this paper, we present a novel deep-learning model for deformable
registration of ultrasound images and an unsupervised approach to training this
model. Our network employs recurrent all-pairs field transforms (RAFT) and a
spatial transformer network (STN) to generate displacement fields at online
rates (apprx. 30 Hz) and accurately track pixel movement. We call our approach
unsupervised recurrent all-pairs field transforms (U-RAFT). In this work, we
use U-RAFT to track pixels in a sequence of ultrasound images to cancel out
respiratory motion in lung ultrasound images. We demonstrate our method on
in-vivo porcine lung videos. We show a reduction of 76% in average pixel
movement in the porcine dataset using respiratory motion compensation strategy.
We believe U-RAFT is a promising tool for compensating different kinds of
motions like respiration and heartbeat in ultrasound images of deformable
tissue.