Deep learning enables fast and accurate quantification of MRI-guided near-infrared spectral tomography for breast cancer diagnosis

The utilization of magnetic resonance (MR) im-aging to guide near-infrared spectral tomography (NIRST) shows significant potential for improving the specificity and sensitivity of breast cancer diagnosis. However, the ef-ficiency and accuracy of NIRST image reconstruction have been limited by the complexities of light propagation mod-eling and MRI image segmentation. To address these chal-lenges, we developed and evaluated a deep learning-based approach for MR-guided 3D NIRST image reconstruction (DL-MRg-NIRST). Using a network trained on synthetic data, the DL-MRg-NIRST system reconstructed images from data acquired during 38 clinical imaging exams of pa-tients with breast abnormalities. Statistical analysis of the results demonstrated a sensitivity of 87.5%, a specificity of 92.9%, and a diagnostic accuracy of 89.5% in distinguishing pathologically defined benign from malignant lesions. Ad-ditionally, the combined use of MRI and DL-MRg-NIRST di-agnoses achieved an area under the receiver operating characteristic (ROC) curve of 0.98. Remarkably, the DL-MRg-NIRST image reconstruction process required only 1.4 seconds, significantly faster than state-of-the-art MR-guided NIRST methods.