Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography

Navchetan Awasthi

Abstract:

Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical
contrast at ultrasonic resolution. Analytical reconstruction
algorithms for photoacoustic (PA) signals require a large
number of data points for accurate image reconstruction.
However, in practical scenarios, data are collected using
the limited number of transducers along with data being
often corrupted with noise resulting in only qualitative
images. Furthermore, the collected boundary data are bandlimited due to limited bandwidth (BW) of the transducer,
making the PA imaging with limited data being qualitative.
In this work, a deep neural network-based model with
loss function being scaled root-mean-squared error was
proposed for super-resolution, denoising, as well as BW
enhancement of the PA signals collected at the boundary
of the domain. The proposed network has been compared
with traditional as well as other popular deep-learning
methods in numerical as well as experimental cases and
is shown to improve the collected boundary data, in turn,
providing superior quality reconstructed PA image. The
improvement obtained in the Pearson correlation, structural
similarity index metric, and root-mean-square error was
as high as 35.62%, 33.81%, and 41.07%, respectively, for
phantom cases and signal-to-noise ratio improvement in
the reconstructed PA images was as high as 11.65 dB
for in vivo cases compared with reconstructed image obtained using original limited BW data. Code is available
at https://sites.google.com/site/sercmig/home/dnnpat.