# End-to-end Ultrasound Frame to Volume Registration

arXiv: https://arxiv.org/abs/2107.06449 · HTML: https://mlsanity.com/paper/2107.06449/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration](https://mlsanity.com/paper/1807.03361/) (2018)
- [Learning Deep Similarity Metric for 3D MR-TRUS Registration](https://mlsanity.com/paper/1806.04548/) (2018)
- [Dilated FCN for Multi-Agent 2D/3D Medical Image Registration](https://mlsanity.com/paper/1712.01651/) (2017)
- [Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning](https://mlsanity.com/paper/2006.07694/) (2020)

## Built on by (most cited first, 13 of 13)

- [Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration](https://mlsanity.com/paper/2406.14534/) (2024)
- [Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration](https://mlsanity.com/paper/2305.06252/) (2023)
- [DreamReg: Belief-Driven World Model for 2D-3D Ultrasound Registration](https://mlsanity.com/paper/2606.18825/) (2026)
- [A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound](https://mlsanity.com/paper/2407.00678/) (2024)
- [Deep Regression 2D-3D Ultrasound Registration for Liver Motion Correction in Focal Tumor Thermal Ablation](https://mlsanity.com/paper/2410.02579/) (2024)
- [A self-attention model for robust rigid slice-to-volume registration of functional MRI](https://mlsanity.com/paper/2404.04546/) (2024)
- [Intraoperative 2D/3D Registration via Spherical Similarity Learning and Differentiable Levenberg-Marquardt Optimization](https://mlsanity.com/paper/2509.06890/) (2025)
- [LayersReg: A Layer-by-Layer Progressive Regressor for Reliable Intraoperative 3D/2D Registration](https://mlsanity.com/paper/2606.26647/) (2026)
- [The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data](https://mlsanity.com/paper/2205.07516/) (2022)
- [A Two-Step Deep Learning Method for 3DCT-2DUS Kidney Registration During Breathing](https://mlsanity.com/paper/2305.13855/) (2023)
- [NeuralBoneReg: An Instance-Specific Label-Free Point Cloud-Based Method for Multi-Modal Bone Surface Registration](https://mlsanity.com/paper/2511.14286/) (2025)
- [Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registration](https://mlsanity.com/paper/2607.23343/) (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)

Fusing intra-operative 2D transrectal ultrasound (TRUS) image with pre-operative 3D magnetic resonance (MR) volume to guide prostate biopsy can significantly increase the yield. However, such a multimodal 2D/3D registration problem is a very challenging task. In this paper, we propose an end-to-end frame-to-volume registration network (FVR-Net), which can efficiently bridge the previous research gaps by aligning a 2D TRUS frame with a 3D TRUS volume without requiring hardware tracking. The proposed FVR-Net utilizes a dual-branch feature extraction module to extract the information from TRUS frame and volume to estimate transformation parameters. We also introduce a differentiable 2D slice sampling module which allows gradients backpropagating from an unsupervised image similarity loss for content correspondence learning. Our model shows superior efficiency for real-time interventional guidance with highly competitive registration accuracy.