  Computed Tomography (CT) is an imaging technique where information about an
object are collected at different angles (called projections or scans). Then
the cross-sectional image showing the internal structure of the slice is
produced by solving an inverse problem. Limited by certain factors such as
radiation dosage, projection angles, the produced images can be noisy or
contain artifacts. Inspired by the success of transformer for natural language
processing, the core idea of this preliminary study is to consider a projection
of 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 we
explore the idea of foundation model by training a masked sinogram model (MSM)
and fine-tune MSM for various downstream applications including CT
reconstruction under data collections restriction (e.g., photon-budget) and a
data-driven solution to approximate solutions of the inverse problem for CT
reconstruction. Models and data used in this study are available at
https://github.com/lzhengchun/TomoTx.
