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Terahertz computed tomography (THz CT) has drawn significant attentionbecause of its unique capability to bring multi-dimensional object informationfrom invisible to visible. However, current physics-model-based THz CTmodalities present low data use efficiency on time-resolved THz signals and lowmodel fusion extensibility, limiting their application fields' practical use.In this paper, we propose a supervised THz deep learning computed tomography(THz DL-CT) framework based on time-domain information. THz DL-CT restoressuperior THz tomographic images of 3D objects by extracting features fromspatio-temporal THz signals without any prior material information. Comparedwith conventional and machine learning based methods, THz DL-CT delivers atleast 50.2%, and 52.6% superior in root mean square error (RMSE) and structuralsimilarity index (SSIM), respectively. Additionally, we have experimentallydemonstrated that the pretrained THz DL-CT model can generalize to reconstructmulti-material systems with no prerequisite information. THz CT through the DLdata fusion approach provides a new pathway for non-invasive functional imagingin object investigation.

Terahertz Spatio-Temporal Deep Learning Computed Tomography