Transformer-based optical attenuation compensation and denoising in photoacoustic imaging

Linear-array-based photoacoustic imaging (PAI) combines functional imaging with structural imaging from ultrasound. However, it suffers from depth-dependent optical attenuation due to surface illumination, resulting in decreased signal amplitude and image contrast with depth. Existing attenuation compensation methods often amplify noise, creating a trade-off between depth enhancement and image quality.

We aim to develop a deep learning method that addresses the coupled problem of optical attenuation compensation and denoising for linear-array-based PAI and test the applicability in vivo.

We propose a vision-transformer-based generative model to address this coupled problem. A diverse dataset was created using simulated data and experimental twin phantoms. Vascular twin phantoms were made by printing digital images onto polyurethane films to test the performance of the model. We trained and compared three deep learning architectures, Pix2Pix, Residual U-Net, and the proposed Transformer U-Net, using various loss functions, including adversarial, MSE, PSNR, SSIM, and a combined PSNR + SSIM. We tested the model on small animal tumor images.

Quantitative evaluation shows that PSNR + SSIM loss is robust in preserving structural details and suppressing noise. Under the pre-specified SSIM + PSNR training objective, Trans U-Net achieves the highest SSIM and PSNR across noise levels on both datasets. In vivo validation using murine breast tumor models and in vivo breast imaging confirmed the model's ability to enhance visualization of deep vascular structures without introducing noise amplification.

The proposed Trans U-Net effectively addresses the coupled problem of attenuation correction and denoising in handheld PAI. This method improves depth-resolved vascular imaging and is potentially useful in clinical and preclinical photoacoustic applications.