End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging.An end-to-end deep neural network, ResU-net, is developed for quantitative photoacoustic imaging.  The contracting and expanding paths enable ResU-net to extract comprehensive context information from multispectral initial pressure images and, subsequently, to infer a quantitative image of chromophore concentration or oxygen saturation (sO2). 
ResU-net
End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging.An end-to-end deep neural network, ResU-net, is developed for quantitative photoacoustic imaging.  The contracting and expanding paths enable ResU-net to extract comprehensive context information from multispectral initial pressure images and, subsequently, to infer a quantitative image of chromophore concentration or oxygen saturation (sO2). 
ResU-net