Development of a Python-based photoacoustic imaging platform with automated instrument control, signal acquisition, and image reconstruction

Photoacoustic imaging combines optical excitation with acoustic detection to obtain spatial information from optically absorbing materials. Implementing such a system requires coordinated control of the excitation source, sample scanning, acoustic signal acquisition, and image reconstruction. In this work, a Python-based photoacoustic imaging platform was developed to integrate these functions into a single automated workflow. The system employed a 405 nm diode laser with pulse-width modulation, an XY scanning stage controlled by an Arduino Uno and TB6560 motor drivers, a condenser microphone for acoustic detection, and an audio interface for signal acquisition. Acoustic signals were acquired at a sampling rate of 44.1 kHz and processed using Fast Fourier Transform (FFT) to extract the response around the laser modulation frequency. System parameters were optimized using soil as a control sample. Frequency scanning from 14,950 to 15,000 Hz resulted in 14,955 Hz being selected as the operating frequency, while variation of the duty cycle from 10% to 90% gave the highest acoustic response at 50%. These parameters were then used to image an irregularly shaped iron sand sample over a 36 × 36 mm scanning area with a 10 × 10 measurement grid. The reconstructed image preserved the main spatial pattern and position of the sample, although the limited number of measurement points restricted the representation of morphological details. The results show that the developed platform can integrate instrument control, acoustic signal acquisition, signal processing, and photoacoustic image reconstruction within a single system.