  Photoacoustic tomography (PAT) is a nonionizing imaging modality capable of
acquiring high contrast and resolution images of optical absorption at depths
greater than traditional optical imaging techniques. Practical considerations
with instrumentation and geometry limit the number of available acoustic
sensors and their view of the imaging target, which result in significant image
reconstruction artifacts degrading image quality. Iterative reconstruction
methods can be used to reduce artifacts but are computationally expensive. In
this work, we propose a novel deep learning approach termed pixelwise deep
learning (PixelDL) that first employs pixelwise interpolation governed by the
physics of photoacoustic wave propagation and then uses a convolution neural
network to directly reconstruct an image. Simulated photoacoustic data from
synthetic vasculature phantom and mouse-brain vasculature were used for
training and testing, respectively. Results demonstrated that PixelDL achieved
comparable performance to iterative methods and outperformed other CNN-based
approaches for correcting artifacts. PixelDL is a computationally efficient
approach that enables for realtime PAT rendering and for improved image
quality, quantification, and interpretation.
