Diffraction　tomography　is　a　promising,　quantitative,　and　nondestructive　three-dimensional　(3D)　imaging　method　that　enables　us　to　obtain　the　complex　refractive　index　distribution　of　a　sample.　The　acquisition　of　the　scattered　fields　under　the　different　illumination　angles　is　a　key　issue,　where　the　complex　scattered　fields　need　to　be　retrieved.　Presently,　in　order　to　develop　terahertz　(THz)　diffraction　tomography,　the　advanced　acquisition　of　the　scattered　fields　is　desired.　In　this　paper,　a　THz　in-line　digital　holographic　diffraction　tomography　(THz-IDHDT)　is　proposed　with　an　extremely　compact　optical　configuration　and　implemented　for　the　first　time,　to　the　best　of　our　knowledge.　A　learning-based　phase　retrieval　algorithm　by　combining　the　physical　model　and　the　convolution　neural　networks,　named　the　physics-enhanced　deep　neural　network　(PhysenNet),　is　applied　to　reconstruct　the　THz　in-line　digital　hologram,　and　obtain　the　complex　amplitude　distribution　of　the　sample　with　high　fidelity.　The　advantages　of　the　PhysenNet　are　that　there　is　no　need　for　pretraining　by　using　a　large　set　of　labeled　data,　and　it　can　also　work　for　thick　samples.　Experimentally　with　a　continuous-wave　THz　laser,　the　PhysenNet　is　first　demonstrated　by　using　the　thin　samples　and　exhibits　superiority　in　terms　of　imaging　quality.　More　importantly,　with　regard　to　the　thick　samples,　PhysenNet　still　works　well,　and　can　offer　2D　complex　scattered　fields　for　diffraction　tomography.　Furthermore,　the　3D　refractive　index　maps　of　two　types　of　foam　sphere　samples　are　successfully　reconstructed　by　the　proposed　method.　For　a　single　foam　sphere,　the　relative　error　of　the　average　refractive　index　value　is　only　0.17%,　compared　to　the　commercial　THz　time-domain　spectroscopy　system.　This　demonstrates　the　feasibility　and　high　accuracy　of　the　THz-IDHDT,　and　the　idea　can　be　applied　to　other　wavebands　as　well.　　©　2023　Chinese　Laser　Press.