Diffuse　optical　tomography　(DOT)　is　a　promising　noninvasive　imaging　method　which　quantifies　optical　parameters　to　achieve　functional　characteristics　of　biological　tissues.　Since　boundary　measurements　are　limited　and　the　light　propagation　in　biological　tissues　is　highly　diffusive,　DOT　image　reconstruction　is　ill-posed　and　ill-conditioned.　Traditional　iterationbased　regularization　methods,　such　as　Tikhonov　regularization,　tend　to　produce　low-quality　images　with　severe　artifacts.　Deep　learning　reconstruction　methods　based　on　convolutional　neural　networks　may　ignore　the　structure　information　of　finite　element　mesh　during　feature　extraction　and　cause　the　loss　of　spatial　features.　In　order　to　overcome　these　problems,　we　propose　a　novel　neural　network　based　on　graph　convolution　to　improve　the　quality　of　reconstructed　DOT　images.　Graph　convolution　can　make　full　use　of　the　spatial　structure　information　of　finite　element　mesh　and　deal　with　the　potential　correlation　between　optical　features　effectively.　To　verify　the　performance　of　the　proposed　algorithm,　simulation　experiments　were　performed.　We　also　compared　the　proposed　algorithm　with　Tikhonov　regularization　method　and　deep　learning　method　based　on　convolutional　neural　network.　Experimental　results　show　that　proposed　algorithm　obtains　better　reconstruction　results　in　terms　of　ABE,　MSE,　PSNR　and　SSIM.　　©　2023　SPIE.