Learning from past scans: Tomographic reconstruction to detect new structures
This addresses the challenge of accurately reconstructing new structures in medical or industrial scans with sparse data, though it is incremental over prior-based methods.
The paper tackles the problem of tomographic reconstruction from sparse measurements, where prior information can dominate new changes, by detecting potential new regions and using weighted priors to reconstruct both old and new structures, resulting in significantly improved overall quality.
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.