Abstract
A novel accelerated model‐based iterative reconstruction strategy for sparse‐view PAT aided by multi‐channel autoencoder priors was proposed. A multi‐channel denoising autoencoder network was designed to learn prior information, which provides constraints for model‐based iterative reconstruction. This integration accelerates the iteration process, leading to optimal reconstruction outcomes. The results show that the proposed method can achieve superior sparse‐view reconstruction with a significant acceleration of iteration. Notably, the proposed method exhibits excellent performance under extremely sparse condition.For further details please visit the article by Xianlin Song, Wenhua Zhong, Zilong Li, Shuchong Peng, Hongyu Zhang, Guijun Wang, Jiaqing Dong, Xuan Liu, Xiaoling Xu, Qiegen Liu (e202300281)image
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