Tomographic reconstruction to detect evolving structures

TL;DR

This paper presents a method for tomographic reconstruction from sparse measurements that leverages prior longitudinal scans to accurately detect evolving structures, balancing between unweighted and weighted priors based on the measurement scenario.

Contribution

The work introduces a novel approach combining unweighted and weighted prior-based reconstructions to effectively detect and reconstruct evolving structures from limited tomographic data.

Findings

Effective reconstruction of old and new structures demonstrated

Weighted priors prevent bias from prior scans in detecting new changes

Validated on both simulated and real tomographic data

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, information from previous longitudinal scans of the same object helps to reconstruct the current object while requiring significantly fewer updating measurements. Our work is based on longitudinal data acquisition scenarios where we wish to study new changes that evolve within an object over time, such as in repeated scanning for disease monitoring, or in tomography-guided surgical procedures. While this is easily feasible when measurements are acquired from a large number of projection views, it is challenging when the number of views is limited. If the goal is to track the changes while simultaneously reducing sub-sampling artefacts, we propose (1) acquiring measurements from a small number of views and using a global unweighted prior-based reconstruction. If the goal is to observe details of new changes, we propose (2) acquiring measurements from a moderate number of views and using a more involved reconstruction routine. We show that in the latter case, a weighted technique is necessary in order to prevent the prior from adversely affecting the reconstruction of new structures that are absent in any of the earlier scans. The reconstruction of new regions is safeguarded from the bias of the prior by computing regional weights that moderate the local influence of the priors. We are thus able to effectively reconstruct both the old and the new structures in the test. In addition to testing on simulated data, we have validated the efficacy of our method on real tomographic data. The results demonstrate the use of both unweighted and weighted priors in different scenarios.