Photoacoustic tomography (PAT), as a novel medical imaging technology, provides structural, functional, and metabolism information of biological tissue in vivo.Sparse Sampling PAT, or SS-PAT, generates images with a smaller number of detectors, yet its image Washing Machine Castor reconstruction is inherently ill-posed.Model-based methods are the state-of-the-art method for SS-PAT image reconstruction, but they require design of complex handcrafted prior.Owing to their ability to derive robust prior from labeled datasets, deep-learning-based methods have achieved great success in solving inverse problems, yet their interpretability is poor.
Herein, we propose a novel SS-PAT image reconstruction method based on deep algorithm unrolling (DAU), which integrates the advantages of model-based and deep-learning-based methods.We firstly provide a thorough analysis of DAU for PAT reconstruction.Then, in order to incorporate the structural prior constraint, we propose a nested DAU framework based on plug-and-play Alternating Direction Method of Multipliers (PnP-ADMM) to deal with the sparse sampling problem.Experimental results on numerical simulation, in Wooden Toys vivo animal imaging, and multispectral un-mixing demonstrate that the proposed DAU image reconstruction framework outperforms state-of-the-art model-based and deep-learning-based methods.