# RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans

arXiv: https://arxiv.org/abs/2404.10766 · HTML: https://mlsanity.com/paper/2404.10766/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [DeepView: View Synthesis with Learned Gradient Descent](https://mlsanity.com/paper/1906.07316/) (2019)
- [Stereo Magnification: Learning View Synthesis using Multiplane Images](https://mlsanity.com/paper/1805.09817/) (2018)
- [Compressible-composable NeRF via Rank-residual Decomposition](https://mlsanity.com/paper/2205.14870/) (2022)
- [ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation](https://mlsanity.com/paper/2109.12108/) (2021)

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

- [TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker](https://mlsanity.com/paper/2506.21765/) (2025)
- [VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos](https://mlsanity.com/paper/2507.21863/) (2025)
- [From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound](https://mlsanity.com/paper/2508.14552/) (2025)
- [Medical Scene Reconstruction and Segmentation based on 3D Gaussian Representation](https://mlsanity.com/paper/2512.22800/) (2025)
- [Rendering Novel Views of MRI Using 3D Gaussian Splatting](https://mlsanity.com/paper/2606.26236/) (2026)
- [AWR-Net: Decoupling Anatomy and Appearance for 3D Fetal Brain Ultrasound Synthesis](https://mlsanity.com/paper/2609.22635/) (2026)
- [Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition](https://mlsanity.com/paper/2609.29373/) (2026)

## Abstract (arXiv)

Two-dimensional (2D) freehand ultrasonography is one of the most commonly used medical imaging modalities, particularly in obstetrics and gynaecology. However, it only captures 2D cross-sectional views of inherently 3D anatomies, losing valuable contextual information. As an alternative to requiring costly and complex 3D ultrasound scanners, 3D volumes can be constructed from 2D scans using machine learning. However this usually requires long computational time. Here, we propose RapidVol: a neural representation framework to speed up slice-to-volume ultrasound reconstruction. We use tensor-rank decomposition, to decompose the typical 3D volume into sets of tri-planes, and store those instead, as well as a small neural network. A set of 2D ultrasound scans, with their ground truth (or estimated) 3D position and orientation (pose) is all that is required to form a complete 3D reconstruction. Reconstructions are formed from real fetal brain scans, and then evaluated by requesting novel cross-sectional views. When compared to prior approaches based on fully implicit representation (e.g. neural radiance fields), our method is over 3x quicker, 46% more accurate, and if given inaccurate poses is more robust. Further speed-up is also possible by reconstructing from a structural prior rather than from scratch.