Improving the quality of diffuse optical tomography reconstruction with APU-Net: an attention-based physical U-Net model

Traditional diffuse optical tomography (DOT) reconstructions suffer from image artifacts due to various factors such as the proximity of DOT sources to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions causing shadowing effects. This study introduces an attention-based U-Net (APU-Net) model with Contextual Transformer (CoT) attention modules to enhance DOT image quality and improve lesion diagnostic accuracy. Trained on simulation and phantom data, and evaluated on clinical data, the APU-Net model effectively reduced artifacts by 26.83% on average and significantly improved image contrast in deeper regions, with increases of 20.28% and 45.31% for the second and third target layers, respectively. These improvements demonstrate the potential of the APU-Net model in enhancing DOT reconstructions for better breast cancer diagnosis.