Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems

TL;DR

This paper introduces a joint reconstruction and low-rank decomposition method using Nonnegative Matrix Factorisation for dynamic inverse problems, enabling efficient processing of highly undersampled data with flexible regularisation.

Contribution

It presents a novel joint approach that combines reconstruction and low-rank decomposition, improving efficiency and flexibility over traditional separate methods.

Findings

Effective reduction in computational complexity for stationary operators

Superior performance compared to PCA-based methods on simulated data

Enables flexible regularisation of spatial and temporal features

Abstract

A primary interest in dynamic inverse problems is to identify the underlying temporal behaviour of the system from outside measurements. In this work we consider the case, where the target can be represented by a decomposition of spatial and temporal basis functions and hence can be efficiently represented by a low-rank decomposition. We then propose a joint reconstruction and low-rank decomposition method based on the Nonnegative Matrix Factorisation to obtain the unknown from highly undersampled dynamic measurement data. The proposed framework allows for flexible incorporation of separate regularisers for spatial and temporal features. For the special case of a stationary operator, we can effectively use the decomposition to reduce the computational complexity and obtain a substantial speed-up. The proposed methods are evaluated for two simulated phantoms and we compare the obtained results to a separate low-rank reconstruction and subsequent decomposition approach based on the widely used principal component analysis.