Robustness of a partially learned photoacoustic reconstruction algorithm

Yoeri Boink, Christoph Brune, Srirang Manohar, Lihong V. Wang, Alexander A. Oraevsky
2019 Photons Plus Ultrasound: Imaging and Sensing 2019  
Classical reconstruction algorithms for photoacoustic tomography (PAT) are mathematically proven to converge, but can be very slow and inadequate with respect to model and data assumptions. With the help of neural networks, learned reconstruction algorithms have recently been developed. These learned algorithms have shown to surpass the reconstruction quality of non-learned ones, but their mathematical analysis is challenging, therefore convergence and stability are not guaranteed. In this
more » ... nteed. In this work, we combine the structure of a well-known model-based algorithm with the efficiency of a datadriven neural network. We show its robustness in simulation against uncertainty and changes in PAT system settings. This is done by varying the placement and calibration of detectors, as well as varying properties of the synthetic phantom, such as geometrical structure and intensity. This yields a robust and computationally efficient algorithm that outperforms classical methods and can be applied to the PAT problem in a universal setting.
doi:10.1117/12.2507446 fatcat:pxxndchjbzdazapc3pjwbvebze