Learning from past scans: Tomographic reconstruction to detect new structures

TL;DR

This paper introduces a method for tomographic reconstruction that leverages prior scans to detect and accurately reconstruct new structures with minimal influence from outdated information, improving quality from sparse measurements.

Contribution

The authors develop a novel approach that detects regions of change and applies regional weights to balance prior influence, enhancing reconstruction accuracy for evolving objects.

Findings

Weighted priors improve reconstruction quality.

Method effectively detects regions with new structures.

Significant reduction in prior dominance over new information.

Abstract

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.