Noise-insensitive defocused signal and resolution enhancement for optical-resolution photoacoustic microscopy via deep learning.

Abstract

Optical-resolution photoacoustic microscopy suffers from narrow depth of field and a significant deterioration in defocused signal intensity and spatial resolution. Here, a method based on deep learning was proposed to enhance the defocused resolution and signal-to-noise ratio. A virtual optical-resolution photoacoustic microscopy based on k-wave was used to obtain the datasets of deep learning with different noise levels. A fully dense U-Net was trained with randomly distributed sources to improve the quality of photoacoustic images. The results show that the PSNR of defocused signal was enhanced by more than 1.2 times. An over 2.6-fold enhancement in lateral resolution and an over 3.4-fold enhancement in axial resolution of defocused regions were achieved. The large volumetric and high-resolution imaging of blood vessels further verified that the proposed method can effectively overcome the deterioration of the signal and the spatial resolution due to the narrow depth of field of optical-resolution photoacoustic microscopy.