  The need for tomographic reconstruction from sparse measurements arises when
the measurement process is potentially harmful, needs to be rapid, or is
uneconomical. In such cases, prior information from previous longitudinal scans
of the same or similar objects helps to reconstruct the current object whilst
requiring significantly fewer `updating' measurements. However, a significant
limitation of all prior-based methods is the possible dominance of the prior
over the reconstruction of new localised information that has evolved within
the test object. In this paper, we improve the state of the art by (1)
detecting potential regions where new changes may have occurred, and (2)
effectively reconstructing both the old and new structures by computing
regional weights that moderate the local influence of the priors. We have
tested the efficacy of our method on synthetic as well as real volume data. The
results demonstrate that using weighted priors significantly improves the
overall quality of the reconstructed data whilst minimising their impact on
regions that contain new information.
