Improving diffuse optical tomography reconstruction using an attention-based U-Net post-processing framework

Diffuse optical tomography (DOT) is a noninvasive imaging technique with promising biomedical applications; however, its reconstruction is severely ill-posed, leading to low spatial resolution, quantitative accuracy, and pronounced robustness when using conventional algorithms. In this study, an attention-enhanced deep learning post-processing method, termed ART-U-Net-CBAM, is proposed to improve DOT image reconstruction. The method combines the physics-based algebraic reconstruction technique (ART) with a U-Net network integrated with a convolutional block attention module (CBAM), enabling adaptive emphasis on informative spatial and channel features. Trained exclusively on simulated data, the proposed network was evaluated using both numerical simulations and phantom experiments involving circular and elliptical targets. Quantitative results demonstrate that ART-U-Net-CBAM consistently outperforms ART and ART-U-Net in terms of reconstruction accuracy, noise robustness, spatial resolution, and structural similarity. These findings indicate that attention-enhanced deep learning post-processing provides an effective and generalizable strategy for enhancing DOT image quality.