Douglas-Rachford Algorithm for Magnetorelaxometry Imaging using Random and Deterministic Activations

TL;DR

This paper introduces a compressed sensing approach with the Douglas-Rachford algorithm for magnetorelaxometry imaging, enabling accurate nanoparticle distribution recovery with fewer activation patterns, thus reducing measurement time.

Contribution

It proposes a novel reconstruction algorithm combining compressed sensing and Douglas-Rachford splitting for improved imaging efficiency and resolution in magnetorelaxometry.

Findings

Accurately recovers nanoparticle distribution with fewer activations

Achieves half the error of traditional methods with fewer measurements

Demonstrates effectiveness on tumor-like phantom data

Abstract

Magnetorelaxometry imaging is a novel tool for quantitative determination of the spatial distribution of magnetic nanoparticle inside an organism. The use of multiple excitation patterns has been demonstrated to significantly improve spatial resolution. However, increasing the number of excitation patterns is considerably more time consuming, because several sequential measurements have to be performed. In this paper, we use compressed sensing in combination with sparse recovery to reduce the total measurement time and to improve spatial resolution. For image reconstruction, we propose using the Douglas-Rachford splitting algorithm applied to the sparse Tikhonov functional including a positivity constraint. Our numerical experiments demonstrate that the resulting algorithm is capable to accurately recover the magnetic nanoparticle distribution from a small number of activation patterns. For example, our algorithm applied with 10 activations yields half the reconstruction error of quadratic Tikhonov regularization applied with 50 activations, for a tumor-like phantom.