Image reconstruction of multispectral sparse sampling photoacoustic tomography based on deep algorithm unrolling

Image reconstruction of multispectral sparse sampling photoacoustic tomography based on deep algorithm unrolling

Blog Article

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 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 ALL 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 ANTI-STRESS 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 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.