Zou Yun, Lin Yixiao, Zhu Quing
Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.
Department of Radiology, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA.
Biomed Opt Express. 2024 Feb 14;15(3):1651-1667. doi: 10.1364/BOE.511807. eCollection 2024 Mar 1.
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.
我们介绍了一种基于深度学习的新型光声层析成像方法，称为光声层析成像神经辐射场（PA-NeRF），用于从有限的二维B扫描数据重建三维体积光声断层扫描（PAT）图像。在传统的三维体积成像中，三维重建需要从各个方向获取的换能器元件数据。我们的模型采用基于神经辐射场（NeRF）的PAT三维重建方法，该方法学习换能器元件位置与相应三维成像之间的关系。与基于卷积的深度学习模型（如Unet和TransUnet）相比，PA-NeRF不学习插值过程，而是从三维光声成像原理中获得见解。此外，我们引入了一种前向损失，以提高重建质量。模拟和体模研究均验证了PA-NeRF的性能。此外，我们将PA-NeRF模型应用于临床实例，以证明其可行性。据我们所知，PA-NeRF是光声层析成像中第一种成功从稀疏B扫描数据重建三维体积的方法。
