ALMA: a mathematics-driven approach for determining tuning parameters in generalized LASSO problems, with applications to MRI

TL;DR

ALMA is a novel, mathematics-driven iterative method that automatically determines tuning parameters for generalized LASSO problems in MRI, improving image reconstruction quality without manual parameter selection.

Contribution

The paper introduces ALMA, a new deterministic technique for selecting tuning parameters in generalized LASSO, applicable to MRI and potentially other fields, enhancing reconstruction reliability.

Findings

ALMA effectively computes tuning parameters during MRI reconstruction.

ALMA improves image quality by optimizing the balance between noise reduction and sparsity.

The method is adaptable to various regularizations and sampling trajectories.

Abstract

Magnetic Resonance Imaging (MRI) is a powerful technique employed for non-invasive in vivo visualization of internal structures. Sparsity is often deployed to accelerate the signal acquisition or overcome the presence of motion artifacts, improving the quality of image reconstruction. Image reconstruction algorithms use TV-regularized LASSO (Total Variation-regularized LASSO) to retrieve the missing information of undersampled signals, by cleaning the data of noise and while optimizing sparsity. A tuning parameter moderates the balance between these two aspects; its choice affecting the quality of the reconstructions. Currently, there is a lack of general deterministic techniques to choose these parameters, which are oftentimes manually selected and thus hinder the reliability of the reconstructions. Here, we present ALMA (Algorithm for Lagrange Multipliers Approximation), an iterative mathematics-inspired technique that computes tuning parameters for generalized LASSO problems during MRI reconstruction. We analyze quantitatively the performance of these parameters for imaging reconstructions via TV-LASSO in an MRI context on phantoms. Although our study concentrates on TV-LASSO, the techniques developed here hold significant promise for a wide array of applications. ALMA is not only adaptable to more generalized LASSO problems but is also robust to accommodate other forms of regularization beyond total variation. Moreover, it extends effectively to handle non-Cartesian sampling trajectories, broadening its utility in complex data reconstruction scenarios. More generally, ALMA provides a powerful tool for numerically solving constrained optimization problems across various disciplines, offering a versatile and impactful solution for advanced computational challenges.