Non-invasive　near-infrared　spectral　tomography　(NIRST)　can　incorporate　the　structural　information　provided　by　simultaneous　magnetic　resonance　imaging　(MRI),　and　this　has　significantly　improved　the　images　obtained　of　tissue　function.　However,　the　process　of　MRI　guidance　in　NIRST　has　been　time　consuming　because　of　the　needs　for　tissue-type　segmentation　and　forward　diffuse　modeling　of　light　propagation.　To　overcome　these　problems,　a　reconstruction　algorithm　for　MRI-guided　NIRST　based　on　deep　learning　is　proposed　and　validated　by　simulation　and　real　patient　imaging　data　for　breast　cancer　characterization.　In　this　approach,　diffused　optical　signals　and　MRI　images　were　both　used　as　the　input　to　the　neural　network,　and　simultaneously　recovered　the　concentrations　of　oxy-hemoglobin,　deoxy-hemoglobin,　and　water　via　end-to-end　training　by　using　20,000　sets　of　computer-generated　simulation　phantoms.　The　simulation　phantom　studies　showed　that　the　quality　of　the　reconstructed　images　was　improved,　compared　to　that　obtained　by　other　existing　reconstruction　methods.　Reconstructed　patient　images　show　that　the　well-trained　neural　network　with　only　simulation　data　sets　can　be　directly　used　for　differentiating　malignant　from　benign　breast　tumors.　(C)　2022　Optica　Publishing　Group　under　the　terms　of　the　Optica　Open　Access　Publishing　Agreement