TY - JOUR
T1 - PA-NeRF, a neural radiance field model for 3D photoacoustic tomography reconstruction from limited Bscan data
AU - Zou, Yun
AU - Lin, Yixiao
AU - Zhu, Quing
N1 - Publisher Copyright:
© 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85186719550&partnerID=8YFLogxK
U2 - 10.1364/BOE.511807
DO - 10.1364/BOE.511807
M3 - Article
C2 - 38495696
AN - SCOPUS:85186719550
SN - 2156-7085
VL - 15
SP - 1651
EP - 1667
JO - Biomedical Optics Express
JF - Biomedical Optics Express
IS - 3
ER -