Complex Valued Long Short Term Memory Based Architecture for Frequency Domain Photoacoustic Imaging

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-valued) to the 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 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 $150^{\circ }$ 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.