Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes

Roberto De Jesus Duarte Coello, Audrey Repetti, Pedro A. Gómez, Mike E. Davies, Yves Wiaux

Research output: Contribution to journalArticle

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 non-convex 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 dened 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 in reconstruction quality. Our approach is validated on the EUROSPIN phantom and on in vivo datasets.
Original languageEnglish
Number of pages32
JournalInverse Problems
Publication statusAccepted/In press - 22 Jul 2019

Fingerprint

Magnetic resonance
Tissue
Data storage equipment
Recovery

Cite this

@article{1adfc6dbc388477eb360da1e13018484,
title = "Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes",
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 non-convex 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 dened 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 in reconstruction quality. Our approach is validated on the EUROSPIN phantom and on in vivo datasets.",
author = "{Duarte Coello}, {Roberto De Jesus} and Audrey Repetti and G{\'o}mez, {Pedro A.} and Davies, {Mike E.} and Yves Wiaux",
year = "2019",
month = "7",
day = "22",
language = "English",
journal = "Inverse Problems",
issn = "0266-5611",
publisher = "Institute of Physics",

}

Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes. / Duarte Coello, Roberto De Jesus; Repetti, Audrey; Gómez, Pedro A.; Davies, Mike E.; Wiaux, Yves.

In: Inverse Problems, 22.07.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes

AU - Duarte Coello, Roberto De Jesus

AU - Repetti, Audrey

AU - Gómez, Pedro A.

AU - Davies, Mike E.

AU - Wiaux, Yves

PY - 2019/7/22

Y1 - 2019/7/22

N2 - 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 non-convex 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 dened 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 in reconstruction quality. Our approach is validated on the EUROSPIN phantom and on in vivo datasets.

AB - 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 non-convex 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 dened 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 in reconstruction quality. Our approach is validated on the EUROSPIN phantom and on in vivo datasets.

M3 - Article

JO - Inverse Problems

JF - Inverse Problems

SN - 0266-5611

ER -