Advancing photoacoustic tomography using deep learning

Abstract

The proposed network was optimized using the simulated data and ix its performance was evaluated on the experimental data obtained using both single and multi-UST PAT systems. The results demonstrate that the proposed approach can enhance the imaging speed in single-UST-PAT systems by ∼6 folds and in the multi-UST-PAT system by ∼2 folds with significant improvement in image quality.Overall, in this thesis we have developed a compact, low-cost desktop PLD-PAT system for pre-clinical application. We have improved its resolution, imaging speed, and also removed artifacts arising from the use of multiple transducers with the help of novel deep learning techniques. We have tested the system's performance with in vivo imaging results and validated the improvements.