Image reconstruction based on graph structure for MRI-guided diffuse optical tomography

Diffuse Optical Tomography (DOT) is a non-invasive, label-free imaging technique widely used in applications such as breast cancer diagnosis and brain imaging. It allows for the quantitative measurement of tissue functional parameters, including the concentrations of oxyhemoglobin, deoxyhemoglobin, and water. However, the quality of reconstructed images is poor due to light scattering. To address this challenge, one effective strategy is to incorporate anatomical information from high-resolution imaging to guide DOT reconstruction. In this study, a new approach (GRI) is developed to leverage MRI images based on graph structure for DOT reconstruction. And its feasibility and effectiveness were evaluated with numerical simulations. The results indicate that GRI significantly improves the Structural Similarity Index (SSIM) of reconstructed total hemoglobin (HbT) images, outperforming conventional Tikhonov regularization and Direct Regularization Imaging (DRI) method by more than 63% and 82%, respectively. These findings are further supported by experiments using real patient data, which demonstrate GRI’s potential in breast cancer diagnosis.