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

Zhang, Limin

doi:10.1364/josaa.589649

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

2026-05-01

0

PRE

AI

DOI：10.1364/josaa.589649

Original

Original request for help

Save

Abstract

En 中文

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 attentionenhanced deep learning post-processing provides an effective and generalizable strategy for enhancing DOT image quality. (c) 2026 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

Keywords:

IMAGE-RECONSTRUCTION

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

Abstract

Journal

Organization

Cited Papers