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Computed Tomography (CT) is an imaging technique where information about anobject are collected at different angles (called projections or scans). Thenthe cross-sectional image showing the internal structure of the slice isproduced by solving an inverse problem. Limited by certain factors such asradiation dosage, projection angles, the produced images can be noisy orcontain artifacts. Inspired by the success of transformer for natural languageprocessing, the core idea of this preliminary study is to consider a projectionof tomography as a word token, and the whole scan of the cross-section (A.K.A.sinogram) as a sentence in the context of natural language processing. Then weexplore the idea of foundation model by training a masked sinogram model (MSM)and fine-tune MSM for various downstream applications including CTreconstruction under data collections restriction (e.g., photon-budget) and adata-driven solution to approximate solutions of the inverse problem for CTreconstruction. Models and data used in this study are available athttps://github.com/lzhengchun/TomoTx.

Masked Sinogram Model with Transformer for ill-Posed Computed Tomography Reconstruction: a Preliminary Study