Real-Time Imaging Enhancement of Handheld Photoacoustic System With FeRAM Crossbar Array Based Neuromorphic Design

The miniaturization and real time imaging capability have always been the desired properties of photoacoustic imaging (PAI) system, which unlocked vast potential for personalized healthcare and diagnostics. While the imaging quality and resolution in such systems are inferior due to physics and system volume constraints, which limited its wide deployment and application. This paper proposes a novel platform to enhance the imaging quality of handheld PAI system in real time, integrating MultiResU-Net imaging enhancement algorithm with Ferroelectric random-access memory (FeRAM) crossbar array. The FeRAM crossbar array enables in memory computing, which is highly suitable for accelerating deep neural network where extensive matrix multiplications are involved. The hardware implementation of the algorithm is optimized for low-power operation on edge devices, a specifically designed algorithmic strategy is further introduced to accurately simulate the impact of hardware variation on the computation in the array with time complexity of O(mn). The feasibility and effectiveness of this method are demonstrated through simulation data (synthesized through physical model) and in vivo data, the experimental results demonstrate more than 10 times of imaging resolution improvement. The execution of neural network inference has been significantly accelerated and can be completed within a few microseconds, fully covering the imaging speed in handheld PAI system and satisfying the real time imaging capability. The whole platform can be integrated into a compact size of 25$\times$25$\times$20 cm³, which is a portable system with real time and high resolution imaging capability.