Tomographic Reconstruction using Global Statistical Prior

TL;DR

This paper introduces a global statistical prior for tomographic reconstruction that leverages eigenspaces built from templates to enhance speed and accuracy in sparse measurement scenarios within a compressive sensing framework.

Contribution

It proposes a novel global prior based on eigenspaces from templates, improving reconstruction speed and accuracy over existing methods.

Findings

Faster reconstruction times compared to state-of-the-art methods.

Lower reconstruction error achieved with the proposed global prior.

Effective across diverse datasets.

Abstract

Recent research in tomographic reconstruction is motivated by the need to efficiently recover detailed anatomy from limited measurements. One of the ways to compensate for the increasingly sparse sets of measurements is to exploit the information from templates, i.e., prior data available in the form of already reconstructed, structurally similar images. Towards this, previous work has exploited using a set of global and patch based dictionary priors. In this paper, we propose a global prior to improve both the speed and quality of tomographic reconstruction within a Compressive Sensing framework. We choose a set of potential representative 2D images referred to as templates, to build an eigenspace; this is subsequently used to guide the iterative reconstruction of a similar slice from sparse acquisition data. Our experiments across a diverse range of datasets show that reconstruction using an appropriate global prior, apart from being faster, gives a much lower reconstruction error when compared to the state of the art.