  A comprehensive guidance view for cardiac interventional surgery can be
provided by the real-time fusion of the intraoperative 2D images and
preoperative 3D volume based on the ultrasound frame-to-volume registration.
However, cardiac ultrasound images are characterized by a low signal-to-noise
ratio and small differences between adjacent frames, coupled with significant
dimension variations between 2D frames and 3D volumes to be registered,
resulting in real-time and accurate cardiac ultrasound frame-to-volume
registration being a very challenging task. This paper introduces a lightweight
end-to-end Cardiac Ultrasound frame-to-volume Registration network, termed
CU-Reg. Specifically, the proposed model leverages epicardium prompt-guided
anatomical clues to reinforce the interaction of 2D sparse and 3D dense
features, followed by a voxel-wise local-global aggregation of enhanced
features, thereby boosting the cross-dimensional matching effectiveness of
low-quality ultrasound modalities. We further embed an inter-frame
discriminative regularization term within the hybrid supervised learning to
increase the distinction between adjacent slices in the same ultrasound volume
to ensure registration stability. Experimental results on the reprocessed CAMUS
dataset demonstrate that our CU-Reg surpasses existing methods in terms of
registration accuracy and efficiency, meeting the guidance requirements of
clinical cardiac interventional surgery.
