Transformer-based optical attenuation compensation and denoising in photoacoustic imaging.

ABSTRACT:

Significance

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.

Aim

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.

Approach

We propose a vision-transformer-based generative model to address this coupled problem. A diverse dataset was created using simulated data and experim

SUBMITTER: Jensen CP 

PROVIDER: S-EPMC12654947 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature