Y-Net: Hybrid deep learning image reconstruction for photoacoustic tomography in vivo.

ABSTRACT: Conventional reconstruction algorithms (e.g., delay-and-sum) used in photoacoustic imaging (PAI) provide a fast solution while many artifacts remain, especially for limited-view with ill-posed problem. In this paper, we propose a new convolutional neural network (CNN) framework Y-Net: a CNN architecture to reconstruct the initial PA pressure distribution by optimizing both raw data and beamformed images once. The network combines two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. We compared our result with some ablation studies, and the results of the test set show better performance compared with conventional reconstruction algorithms and other deep learning method (U-Net). Both in-vitro and in-vivo experiments are used to va

SUBMITTER: Lan H 

PROVIDER: S-EPMC7322183 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature