Deep Image Prior for Sparse-sampling Photoacoustic Microscopy

Photoacoustic microscopy (PAM) is an emerging method for imaging both
structural and functional information without the need for exogenous contrast
agents. However, state-of-the-art PAM faces a tradeoff between imaging speed
and spatial sampling density within the same field-of-view (FOV). Limited by
the pulsed laser's repetition rate, the imaging speed is inversely proportional
to the total number of effective pixels. To cover the same FOV in a shorter
amount of time with the same PAM hardware, there is currently no other option
than to decrease spatial sampling density (i.e., sparse sampling). Deep
learning methods have recently been used to improve sparsely sampled PAM
images; however, these methods often require time-consuming pre-training and a
large training dataset that has fully sampled, co-registered ground truth. In
this paper, we propose using a method known as "deep image prior" to improve
the image quality of sparsely sampled PAM images. The network does not need
prior learning or fully sampled ground truth, making its implementation more
flexible and much quicker. Our results show promising improvement in PA
vasculature images with as few as 2% of the effective pixels. Our deep image
prior approach produces results that outperform interpolation methods and can
be readily translated to other high-speed, sparse-sampling imaging modalities.