Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes

TL;DR

This paper introduces GAP-MRF, a scalable greedy algorithm for improving tissue parameter estimation in Magnetic Resonance Fingerprinting affected by Partial Volume Effect, validated through simulations and real data.

Contribution

It proposes a novel greedy approximate projected gradient descent method for PVE correction in MRF, enhancing accuracy and scalability over existing techniques.

Findings

Outperforms state-of-the-art methods in synthetic PVE simulations

Successfully validated on EUROSPIN phantom data

Effective in in vivo datasets with phase error compensation

Abstract

In quantitative Magnetic Resonance Imaging, traditional methods suffer from the so-called Partial Volume Effect (PVE) due to spatial resolution limitations. As a consequence of PVE, the parameters of the voxels containing more than one tissue are not correctly estimated. Magnetic Resonance Fingerprinting (MRF) is not an exception. The existing methods addressing PVE are neither scalable nor accurate. We propose to formulate the recovery of multiple tissues per voxel as a nonconvex constrained least-squares minimisation problem. To solve this problem, we develop a memory efficient, greedy approximate projected gradient descent algorithm, dubbed GAP-MRF. Our method adaptively finds the regions of interest on the manifold of fingerprints defined by the MRF sequence. We generalise our method to compensate for phase errors appearing in the model, using an alternating minimisation approach. We show, through simulations on synthetic data with PVE, that our algorithm outperforms state-of-the-art methods. Our approach is validated on the EUROSPIN phantom and on in vivo datasets.