Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration
Long Lei, Jun Zhou, Jialun Pei, Baoliang Zhao, Yueming Jin, Yuen-Chun, Jeremy Teoh, Jing Qin, Pheng-Ann Heng

TL;DR
This paper presents CU-Reg, a lightweight neural network that improves real-time 2D-3D cardiac ultrasound registration accuracy by leveraging epicardium prompts and inter-frame regularization, aiding cardiac surgery guidance.
Contribution
Introduction of CU-Reg, a novel end-to-end network utilizing epicardium prompts and regularization for enhanced ultrasound frame-to-volume registration.
Findings
Outperforms existing methods in accuracy and efficiency
Effective in low-quality ultrasound modalities
Meets clinical guidance requirements
Abstract
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…
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Taxonomy
TopicsCardiac Imaging and Diagnostics
MethodsDiscriminative Regularization
