1 research outputs found
Photoacoustic Microscopy with Sparse Data Enabled by Convolutional Neural Networks for Fast Imaging
Photoacoustic microscopy (PAM) has been a promising biomedical imaging
technology in recent years. However, the point-by-point scanning mechanism
results in low-speed imaging, which limits the application of PAM. Reducing
sampling density can naturally shorten image acquisition time, which is at the
cost of image quality. In this work, we propose a method using convolutional
neural networks (CNNs) to improve the quality of sparse PAM images, thereby
speeding up image acquisition while keeping good image quality. The CNN model
utilizes both squeeze-and-excitation blocks and residual blocks to achieve the
enhancement, which is a mapping from a 1/4 or 1/16 low-sampling sparse PAM
image to a latent fully-sampled image. The perceptual loss function is applied
to keep the fidelity of images. The model is mainly trained and validated on
PAM images of leaf veins. The experiments show the effectiveness of our
proposed method, which significantly outperforms existing methods
quantitatively and qualitatively. Our model is also tested using in vivo PAM
images of blood vessels of mouse ears and eyes. The results show that the model
can enhance the image quality of the sparse PAM image of blood vessels from
several aspects, which may help fast PAM and facilitate its clinical
applications.Comment: 13 pages (including 2 pages of supplementary materials