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.

通过超声帧到体积的实时融合，基于术中 2D 图像和术前 3D 体积，提供了一个全面的心脏介入手术指导视图。然而，由于心脏超声图像在信噪比低、相邻帧之间差异小，以及 2D 帧和 3D 体积之间尺寸变化显著，因此实现实时准确的心脏超声帧到体积的配准是一项非常具有挑战性的任务。本文提出了一种轻量级的端到端心脏超声帧到体积配准网络，称为 CU-Reg。具体而言，该模型利用心外膜引导的解剖线索来加强 2D 稀疏和 3D 密集特征的交互作用，然后通过体素级的局部全局特征汇聚增强特征，从而提高低质量超声模态的跨维度匹配效果。我们进一步在混合监督学习中嵌入了帧间判别性正则化项，以增加同一超声体积中相邻切片之间的区别，确保配准的稳定性。在重新处理的 CAMUS 数据集上的实验结果表明，我们的 CU-Reg 在配准精度和效率方面超过了现有方法，满足了临床心脏介入手术的指导要求。