Dynamic Tomography Reconstruction by Projection-Domain Separable Modeling

TL;DR

This paper introduces a novel method for dynamic tomography that reconstructs a spatio-temporal object representation from sequential projections, addressing the challenges of inconsistency and instability in the inverse problem.

Contribution

It presents a new projection-domain separable modeling approach, analyzes conditions for unique and stable solutions, and validates an effective recovery algorithm experimentally.

Findings

The proposed method achieves stable reconstructions in dynamic tomography.

It outperforms GMLR-based deep prior methods in experiments.

The approach effectively reconstructs movies from sequential projection data.

Abstract

In dynamic tomography the object undergoes changes while projections are being acquired sequentially in time. The resulting inconsistent set of projections cannot be used directly to reconstruct an object corresponding to a time instant. Instead, the objective is to reconstruct a spatio-temporal representation of the object, which can be displayed as a movie. We analyze conditions for unique and stable solution of this ill-posed inverse problem, and present a recovery algorithm, validating it experimentally. We compare our approach to one based on the recently proposed GMLR variation on deep prior for video, demonstrating the advantages of the proposed approach.