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1. Complex Valued Long Short Term
Memory Based Architecture for
Frequency Domain Photoacoustic
Imaging
Abstract
Frequency domain photoacoustic (FDPA) imaging has great potential in a
clinical setting compared to time
reduced cost and small form factors. However FDPA system struggles with
lower signal to noise ratio, necessitating the need for advanced image
reconstruction methods. Most of the image reconstruction approaches in
FDPA imaging are based
emphasis has been placed on developing deep learning based approaches
for FDPA imaging. In this work, a image translation network was developed
with the ability to directly map from sinogram data (complex
initial pressure rise distribution (real
architecture was based on a Long Short Term Memory (LSTM) backbone
(with adjoined real and imaginary parts of the complex sinogram data as
Complex Valued Long Short Term
Memory Based Architecture for
Frequency Domain Photoacoustic
Frequency domain photoacoustic (FDPA) imaging has great potential in a
clinical setting compared to time-domain photoacoustic imaging due to its
reduced cost and small form factors. However FDPA system struggles with
lower signal to noise ratio, necessitating the need for advanced image
reconstruction methods. Most of the image reconstruction approaches in
FDPA imaging are based on analytical or model based schemes. Very less
emphasis has been placed on developing deep learning based approaches
for FDPA imaging. In this work, a image translation network was developed
with the ability to directly map from sinogram data (complex-va
initial pressure rise distribution (real-valued) for FD-PA imaging. This
architecture was based on a Long Short Term Memory (LSTM) backbone
(with adjoined real and imaginary parts of the complex sinogram data as
Complex Valued Long Short Term
Frequency Domain Photoacoustic
Frequency domain photoacoustic (FDPA) imaging has great potential in a
imaging due to its
reduced cost and small form factors. However FDPA system struggles with
lower signal to noise ratio, necessitating the need for advanced image
reconstruction methods. Most of the image reconstruction approaches in
on analytical or model based schemes. Very less
emphasis has been placed on developing deep learning based approaches
for FDPA imaging. In this work, a image translation network was developed
valued) to the
PA imaging. This
architecture was based on a Long Short Term Memory (LSTM) backbone
(with adjoined real and imaginary parts of the complex sinogram data as

2. input) followed by a fully connected layer, which is then passed through a
convolution and transposed convolution layer pair. The result of the FDPA-
LSTM architecture was compared with direct translational networks based on
ResNet, UNet and AUTOMAP and found to have an improvement of about
15% in terms of PSNR and 10% in terms of SSIM with 150∘ data acquisition
limited-view angle. Further, the FDPA-LSTM was also compared with post-
processing UNet architecture on backprojection and Tikhonov regularized
reconstruction. A 20% improvement in terms of PSNR with backprojection and
post-processing UNet was observed. Further FDPA-LSTM had similar
performance as Tikhonov and post-processing UNet (with 75 times
acceleration). The developed scheme will indeed be very useful for achieving
accelerated and accurate frequency domain photoacoustic imaging.