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Platform-Flexible Deep Learning Driven Acoustic Resolution Photoacoustic Microscopy: Expediting Scanning Speed and Reducing Photobleaching Effects
  • Avijit Paul,
  • Christopher Nguyen,
  • Srivalleesha Mallidi
Avijit Paul
Tufts University
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Christopher Nguyen
Tufts University
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Srivalleesha Mallidi
Tufts University

Corresponding Author:[email protected]

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Abstract

In acoustic resolution PA microscopy (AR-PAM), high signal-to-noise ratio (SNR) images require averaging pulse-illuminated A-scans, slowing data acquisition. Multi-wavelength PA imaging further reduces scan speed, leading to issues like photobleaching of exogenous contrast agents due to prolonged exposure. Traditional noise removal algorithms fall short, while deep learning models like U-Net, though effective, also compromise image contrast. We propose a platform-flexible denoising conditional GAN (modified Pix2Pix) to generate high SNR images with single pulse illumination, reducing AR-PAM scan time by 30-fold. Tested across various systems, our model minimizes photobleaching, enhances efficiency, and remains adaptable to diverse hardware setups without retraining, broadening its preclinical and clinical applications.