# Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration

arXiv: https://arxiv.org/abs/2406.14534 · HTML: https://mlsanity.com/paper/2406.14534/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [Transformer Quality in Linear Time](https://mlsanity.com/paper/2202.10447/) (2022)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI](https://mlsanity.com/paper/2206.10802/) (2022)
- [Slice-to-volume medical image registration: a survey](https://mlsanity.com/paper/1702.01636/) (2017)
- [Cross-modal Attention for MRI and Ultrasound Volume Registration](https://mlsanity.com/paper/2107.04548/) (2021)
- [End-to-end Ultrasound Frame to Volume Registration](https://mlsanity.com/paper/2107.06449/) (2021)
- [Global Multi-modal 2D/3D Registration via Local Descriptors Learning](https://mlsanity.com/paper/2205.03439/) (2022)
- [Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion](https://mlsanity.com/paper/1702.08891/) (2017)
- [Deep Learning for Medical Image Registration: A Comprehensive Review](https://mlsanity.com/paper/2204.11341/) (2022)
- [Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields](https://mlsanity.com/paper/1608.05562/) (2016)

## Built on by (most cited first, 7 of 7)

- [Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection](https://mlsanity.com/paper/2504.15152/) (2025)
- [DreamReg: Belief-Driven World Model for 2D-3D Ultrasound Registration](https://mlsanity.com/paper/2606.18825/) (2026)
- [LayersReg: A Layer-by-Layer Progressive Regressor for Reliable Intraoperative 3D/2D Registration](https://mlsanity.com/paper/2606.26647/) (2026)
- [CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking](https://mlsanity.com/paper/2601.02521/) (2026)
- [Preoperative-to-intraoperative Liver Registration for Laparoscopic Surgery via Latent-Grounded Correspondence Constraints](https://mlsanity.com/paper/2603.01720/) (2026)
- [Point-Wise Geometry-Aware Transformer for Partial-to-Full Point Cloud Registration in Computer-Assisted Surgery](https://mlsanity.com/paper/2606.13488/) (2026)
- [SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimation](https://mlsanity.com/paper/2608.28715/) (2026)

## Abstract (arXiv)

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.