ArticleBiomedical optics express2024
PA-NeRF, a neural radiance field model for 3D photoacoustic tomography reconstruction from limited Bscan data.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Super Time-Resolved Tomography.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multi-wavelength graph convolutional network for high-performance sparse multispectral optoacoustic tomography.Photoacoustics · 2025Article
- Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images.Photoacoustics · 2025Article
- SonoNERFs: Neural Radiance Fields Applied to Biological Echolocation Systems Allow 3D Scene Reconstruction through Perceptual Prediction.Biomimetics (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We introduce a novel deep-learning-based photoacoustic tomography method called Photoacoustic Tomography Neural Radiance Field (PA-NeRF) for reconstructing 3D volumetric PAT images from limited 2D Bscan data. In conventional 3D volumetric imaging, a 3D reconstruction requires transducer element data obtained from all directions. Our model employs a NeRF-based PAT 3D reconstruction method, which learns the relationship between transducer element positions and the corresponding 3D imaging. Compared with convolution-based deep-learning models, such as Unet and TransUnet, PA-NeRF does not learn the interpolation process but rather gains insight from 3D photoacoustic imaging principles. Additionally, we introduce a forward loss that improves the reconstruction quality. Both simulation and phantom studies validate the performance of PA-NeRF. Further, we apply the PA-NeRF model to clinical examples to demonstrate its feasibility. To the best of our knowledge, PA-NeRF is the first method in photoacoustic tomography to successfully reconstruct a 3D volume from sparse Bscan data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.