Reduction of photobleaching effects in photoacoustic imaging using noise agnostic, platform-flexible deep-learning methods.

Abstract

Significance: Molecular photoacoustic (PA) imaging with exogenous dyes faces a significant challenge due to the photobleaching of the dye that can compromise tissue visualization, particularly in 3D imaging. Addressing this limitation can revolutionize the field by enabling safer, more reliable imaging and improve real-time visualization, quantitative analysis, and clinical decision-making in various molecular PA imaging applications such as image-guided surgeries.

Aim: We tackle photobleaching in molecular PA imaging by introducing a platform-flexible deep learning framework that enhances SNR from single-laser pulse data, preserving contrast and signal integrity without requiring averaging of signals from multiple laser pulses.

Approach: The generative deep learning network was trained with an LED-illuminated PA image dataset and tested on acoustic resolution PA microscopy images obtained with single-laser pulse illumination. In vitro and ex vivo samples were first tested for demonstrating SNR improvement, and then, a 3D-scanning experiment with an ICG-filled tube was conducted to depict the usability of the technique in reducing the impact of photobleaching during PA imaging.

Results: Our generative deep learning model outperformed traditional nonlearning, filter-based algorithms and the U-Net deep learning network when tested with in vitro and ex vivo single pulse-illuminated images, showing superior performance in terms of signal-to-noise ratio ( $93.54\pm 6.07$ , and $92.77\pm 10.74$ compared with $86.35\pm 3.97$ , and $84.52\pm 11.82$ with U-Net for kidney, and tumor, respectively) and contrast-to-noise ratio ( $11.82\pm 4.42$ , and $9.9\pm 4.41$ compared with $7.59\pm 0.82$ , and $6.82\pm 2.12$ with U-Net for kidney, and tumor respectively). The use of cGAN with single-pulse rapid imaging has the potential to prevent photobleaching ( $9.51\pm 3.69\%$ with cGAN, and $35.14\pm 5.38\%$ with long-time laser exposure by averaging 30 pulses), enabling accurate, quantitative imaging suitable for real-time implementation, and improved clinical decision support.

Conclusions: We demonstrate the potential of a platform-flexible generative deep learning-based approach to mitigate the effects of photobleaching in PA imaging by enhancing signal-to-noise ratio from single pulse-illuminated data, thereby improving image quality and preserving contrast in real time.