Android mobile-platform-based image reconstruction for photoacoustic tomography.

Significance: In photoacoustic tomography (PAT), numerous reconstruction algorithms have been utilized to recover initial pressure rise distribution from the acquired pressure waves. In practice, most of these reconstructions are carried out on a desktop/workstation and the mobile-based reconstructions are far-flung. In recent years, mobile phones are becoming so ubiquitous, and most of them encompass a higher computing ability. Hence, realizing PAT image reconstruction on a mobile platform is intrinsic, and it will enhance the adaptability of PAT systems with point-of-care applications.

Aim: To implement PAT image reconstruction in Android-based mobile platforms.

Approach: For implementing PAT image reconstruction in Android-based mobile platforms, we proposed an Android-based application using Python to perform beamforming process in Android phones.

Results: The performance of the developed application was analyzed on different mobile platforms using both simulated and experimental datasets. The results demonstrate that the developed algorithm can accomplish the image reconstruction of small animal brain dataset in 2.4 s. Furthermore, the developed application reconstructs PAT images with comparable speed and no loss of image quality compared to that on a laptop. Employing a two-fold downsampling procedure could serve as a viable solution for reducing the time needed for beamforming while preserving image quality with minimal degradation.

Conclusions: We proposed an Android-based application that achieves image reconstruction on cheap, small, and universally available phones instead of relatively bulky expensive desktop computers/laptops/workstations. A beamforming speed of 2.4 s is achieved without hampering the quality of the reconstructed image.