  X-Ray based computed tomography (CT) is a well-established technique for
determining the three-dimensional structure of an object from its
two-dimensional projections. In the past few decades, there have been
significant advancements in the brightness and detector technology of
tomography instruments at synchrotron sources. These advancements have led to
the emergence of new observations and discoveries, with improved capabilities
such as faster frame rates, larger fields of view, higher resolution, and
higher dimensionality. These advancements have enabled the material science
community to expand the scope of tomographic measurements towards increasingly
in-situ and in-operando measurements. In these new experiments, samples can be
rapidly evolving, have complex geometries, and restrictions on the field of
view, limiting the number of projections that can be collected. In such cases,
standard filtered back-projections (FBP) for the reconstructions often result
in poor-quality reconstructions. Iterative reconstruction algorithms, such as
model-based iterative reconstructions (MBIR), have demonstrated considerable
success in producing high-quality reconstructions under such restrictions, but
typically require high-performance computing resources with hundreds of compute
nodes to solve the problem in a reasonable time.
