# ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation

arXiv: https://arxiv.org/abs/2109.12108 · HTML: https://mlsanity.com/paper/2109.12108/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks](https://mlsanity.com/paper/1911.07566/) (2019)

## Built on by (most cited first, 16 of 16)

- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [DINER: Disorder-Invariant Implicit Neural Representation](https://mlsanity.com/paper/2211.07871/) (2022)
- [Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging](https://mlsanity.com/paper/2301.10520/) (2023)
- [Disorder-invariant Implicit Neural Representation](https://mlsanity.com/paper/2304.00837/) (2023)
- [UltraGauss: Ultrafast Gaussian Reconstruction of 3D Ultrasound Volumes](https://mlsanity.com/paper/2505.05643/) (2025)
- [RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans](https://mlsanity.com/paper/2404.10766/) (2024)
- [NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild](https://mlsanity.com/paper/2408.10258/) (2024)
- [Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images](https://mlsanity.com/paper/2209.05477/) (2022)
- [UlRe-NeRF: 3D Ultrasound Imaging through Neural Rendering with Ultrasound Reflection Direction Parameterization](https://mlsanity.com/paper/2408.00860/) (2024)
- [MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2506.15835/) (2025)
- [3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices](https://mlsanity.com/paper/2507.02411/) (2025)
- [LiverUSRecon: Automatic 3D Reconstruction and Volumetry of the Liver with a Few Partial Ultrasound Scans](https://mlsanity.com/paper/2406.19336/) (2024)
- [Compact Implicit Neural Representations for Plane Wave Images](https://mlsanity.com/paper/2409.11370/) (2024)
- [X2BR: High-Fidelity 3D Bone Reconstruction from a Planar X-Ray Image with Hybrid Neural Implicit Methods](https://mlsanity.com/paper/2504.08675/) (2025)
- [From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound](https://mlsanity.com/paper/2508.14552/) (2025)
- [UltraG-Ray: Physics-Based Gaussian Ray Casting for Novel Ultrasound View Synthesis](https://mlsanity.com/paper/2603.29022/) (2026)

## Abstract (arXiv)

The objective of this work is to achieve sensorless reconstruction of a 3D volume from a set of 2D freehand ultrasound images with deep implicit representation. In contrast to the conventional way that represents a 3D volume as a discrete voxel grid, we do so by parameterizing it as the zero level-set of a continuous function, i.e. implicitly representing the 3D volume as a mapping from the spatial coordinates to the corresponding intensity values. Our proposed model, termed as ImplicitVol, takes a set of 2D scans and their estimated locations in 3D as input, jointly refining the estimated 3D locations and learning a full reconstruction of the 3D volume. When testing on real 2D ultrasound images, novel cross-sectional views that are sampled from ImplicitVol show significantly better visual quality than those sampled from existing reconstruction approaches, outperforming them by over 30% (NCC and SSIM), between the output and ground-truth on the 3D volume testing data. The code will be made publicly available.