  This work is concerned with applying iterative image reconstruction, based on
constrained total-variation minimization, to low-intensity X-ray CT systems
that have a high sampling rate. Such systems pose a challenge for iterative
image reconstruction, because a very fine image grid is needed to realize the
resolution inherent in such scanners. These image arrays lead to
under-determined imaging models whose inversion is unstable and can result in
undesirable artifacts and noise patterns. There are many possibilities to
stabilize the imaging model, and this work proposes a method which may have an
advantage in terms of algorithm efficiency. The proposed method introduces
additional constraints in the optimization problem; these constraints set to
zero high spatial frequency components which are beyond the sensing capability
of the detector. The method is demonstrated with an actual CT data set and
compared with another method based on projection up-sampling.
