Sparse Dictionary-Based Solution of Dynamic Inverse Problems

TL;DR

This paper introduces a sparse dictionary-based approach for solving dynamic inverse problems, leveraging spatial and temporal features to improve reconstruction quality, especially with limited data, and demonstrates its effectiveness on real-world CT and MRI datasets.

Contribution

It proposes a stochastic hierarchical sparsity prior and an efficient MAP estimation method using IAS, offering a competitive alternative to ADMM with less hyper-parameter sensitivity.

Findings

Method performs well on real CT and MRI data.

Lower sensitivity to hyper-parameter tuning compared to ADMM.

Competitive results in compressed sensing applications.

Abstract

In ill-posed dynamic inverse problems expected spatial features and temporal correlation between frames can be leveraged to improve the quality of the computed solution, in particular when the available data are limited and the dimensionality of the unknown is large. One way to take advantage of the spatial and temporal traits believed to characterize the solution is to encode them into the entries of a dictionary, and to seek the solution as a sparse linear combination of the dictionary atoms. To promote a vector of coefficients with mostly vanishing entries, we consider a stochastic extension of the dictionary coding problem model with a random hierarchical sparsity promoting prior. We compute the Maximum A Posteriori (MAP) estimate of the coefficient vector using the Iterative Alternating Sequential Algorithm (IAS), which has been demonstrated to efficiently solve inverse problems with minimal need for parameter tuning. The proposed methodology is tested on real-world dynamic Computed Tomography and MRI datasets, where it is compared to the popular Alternating Direction Method of Minimizers (ADMM). The computed examples show the that proposed methodology is competitive with the ADMM for compressed sensing, with a significantly lower sensitivity to hyper-parameter selection.