Novel image optimization on photoacoustic tomography

Photoacoustic tomography (PAT) of biological tissue offers potential advantages in distinguishing different structures according to their chemical composition. Yet the detected photoacoustic signals are usually of low signal-to-noise ratio (SNR), which seriously deteriorates the image quality in PAT. This paper introduces an improved multi-sample based adaptive approach to solve such a problem. With this method, we are capable of reducing image noise dramatically and in addition, some detail information that used to be buried in the background noise can be discovered. We carry out both ex vivo and in vivo experiments to validate the effectiveness of our proposed method.

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