# Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging

arXiv: https://arxiv.org/abs/2301.10520 · HTML: https://mlsanity.com/paper/2301.10520/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019)
- [INeRF: Inverting Neural Radiance Fields for Pose Estimation](https://mlsanity.com/paper/2012.05877/) (2020)
- [Advances in Neural Rendering](https://mlsanity.com/paper/2111.05849/) (2021)
- [Urban Radiance Fields](https://mlsanity.com/paper/2111.14643/) (2021)
- [Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery](https://mlsanity.com/paper/2206.15255/) (2022)
- [MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray](https://mlsanity.com/paper/2202.01020/) (2022)
- [Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry](https://mlsanity.com/paper/2108.10118/) (2021)
- [ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation](https://mlsanity.com/paper/2109.12108/) (2021)
- [Polarimetric Pose Prediction](https://mlsanity.com/paper/2112.03810/) (2021)
- [Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation](https://mlsanity.com/paper/2006.10166/) (2020)

## Built on by (most cited first, 27 of 27)

- [DaReNeRF: Direction-aware Representation for Dynamic Scenes](https://mlsanity.com/paper/2403.02265/) (2024)
- [Neural Radiance Field-based Visual Rendering: A Comprehensive Review](https://mlsanity.com/paper/2404.00714/) (2024)
- [Radar Fields: An Extension of Radiance Fields to SAR](https://mlsanity.com/paper/2312.12961/) (2023)
- [Neural Radiance Fields in Medical Imaging: A Survey](https://mlsanity.com/paper/2402.17797/) (2024)
- [NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild](https://mlsanity.com/paper/2408.10258/) (2024)
- [UltraRay: Introducing Full-Path Ray Tracing in Physics-Based Ultrasound Simulation](https://mlsanity.com/paper/2501.05828/) (2025)
- [UlRe-NeRF: 3D Ultrasound Imaging through Neural Rendering with Ultrasound Reflection Direction Parameterization](https://mlsanity.com/paper/2408.00860/) (2024)
- [UltrON: Ultrasound Occupancy Networks](https://mlsanity.com/paper/2509.08991/) (2025)
- [MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging](https://mlsanity.com/paper/2509.16806/) (2025)
- [NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity](https://mlsanity.com/paper/2406.10212/) (2024)
- [OSCAR: Occupancy-based Shape Completion via Acoustic Neural Implicit Representations](https://mlsanity.com/paper/2603.08279/) (2026)
- [3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices](https://mlsanity.com/paper/2507.02411/) (2025)
- [M-Gaussian: An Magnetic Gaussian Framework for Efficient Multi-Stack MRI Reconstruction](https://mlsanity.com/paper/2603.00145/) (2026)
- [PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement](https://mlsanity.com/paper/2408.03657/) (2024)
- [Compact Implicit Neural Representations for Plane Wave Images](https://mlsanity.com/paper/2409.11370/) (2024)
- [DaRePlane: Direction-aware Representations for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2410.14169/) (2024)
- [Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation](https://mlsanity.com/paper/2502.08634/) (2025)
- [ImplicitCell: Resolution Cell Modeling of Joint Implicit Volume Reconstruction and Pose Refinement in Freehand 3D Ultrasound](https://mlsanity.com/paper/2503.06686/) (2025)
- [IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays](https://mlsanity.com/paper/2504.14699/) (2025)
- [Beyond Anonymization: Object Scrubbing for Privacy-Preserving 2D and 3D Vision Tasks](https://mlsanity.com/paper/2504.16557/) (2025)
- [UltraGS: Real-Time Physically-Decoupled Gaussian Splatting for Ultrasound Novel View Synthesis](https://mlsanity.com/paper/2511.07743/) (2025)
- [Broadening View Synthesis of Dynamic Scenes from Constrained Monocular Videos](https://mlsanity.com/paper/2512.14406/) (2025)
- [Medical Scene Reconstruction and Segmentation based on 3D Gaussian Representation](https://mlsanity.com/paper/2512.22800/) (2025)
- [UltraG-Ray: Physics-Based Gaussian Ray Casting for Novel Ultrasound View Synthesis](https://mlsanity.com/paper/2603.29022/) (2026)
- [Multivariate Gaussian NeRF for Wide Field-of-View Ultrasound Reconstruction](https://mlsanity.com/paper/2604.24187/) (2026)
- [UBone3D: Physics-Rectified Conditional Flow Matching for Anatomical 3D Shape Completion from Ultrasound](https://mlsanity.com/paper/2609.11506/) (2026)
- [Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition](https://mlsanity.com/paper/2609.29373/) (2026)

## Abstract (arXiv)

We present a physics-enhanced implicit neural representation (INR) for ultrasound (US) imaging that learns tissue properties from overlapping US sweeps. Our proposed method leverages a ray-tracing-based neural rendering for novel view US synthesis. Recent publications demonstrated that INR models could encode a representation of a three-dimensional scene from a set of two-dimensional US frames. However, these models fail to consider the view-dependent changes in appearance and geometry intrinsic to US imaging. In our work, we discuss direction-dependent changes in the scene and show that a physics-inspired rendering improves the fidelity of US image synthesis. In particular, we demonstrate experimentally that our proposed method generates geometrically accurate B-mode images for regions with ambiguous representation owing to view-dependent differences of the US images. We conduct our experiments using simulated B-mode US sweeps of the liver and acquired US sweeps of a spine phantom tracked with a robotic arm. The experiments corroborate that our method generates US frames that enable consistent volume compounding from previously unseen views. To the best of our knowledge, the presented work is the first to address view-dependent US image synthesis using INR.