Unsupervised Deformable Image Registration for Respiratory Motion Compensation in Ultrasound Images
This addresses motion artifacts in ultrasound imaging for medical applications, but it is incremental as it adapts existing methods to a specific domain.
The paper tackled the problem of respiratory motion compensation in lung ultrasound images by developing an unsupervised deep-learning model called U-RAFT, which reduced average pixel movement by 76% in porcine lung videos.
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.