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Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting. / Nagtegaal, Martijn; Koken, Peter; Amthor, Thomas; Doneva, Mariya.

In: Magnetic Resonance in Medicine, Vol. 83, No. 2, 2020, p. 521-534.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Nagtegaal, M, Koken, P, Amthor, T & Doneva, M 2020, 'Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting' Magnetic Resonance in Medicine, vol. 83, no. 2, pp. 521-534. https://doi.org/10.1002/mrm.27947

APA

Nagtegaal, M., Koken, P., Amthor, T., & Doneva, M. (2020). Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting. Magnetic Resonance in Medicine, 83(2), 521-534. https://doi.org/10.1002/mrm.27947

Vancouver

Author

Nagtegaal, Martijn ; Koken, Peter ; Amthor, Thomas ; Doneva, Mariya. / Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting. In: Magnetic Resonance in Medicine. 2020 ; Vol. 83, No. 2. pp. 521-534.

BibTeX

@article{694a17a9a7d84c9ba54c9c86a2ee7319,
title = "Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting",
abstract = "PurposeTo develop an efficient algorithm for multi‐component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties.MethodsDifferent tissues or components within a voxel are potentially separable in MRF because of their distinct signal evolutions. The observed signal evolution in each voxel can be described as a linear combination of the signals for each component with a non‐negative weight. An assumption that only a small number of components are present in the measured field of view is usually imposed in the interpretation of multi‐component data. In this work, a joint sparsity constraint is introduced to utilize this additional prior knowledge in the multi‐component analysis of MRF data. A new algorithm combining joint sparsity and non‐negativity constraints is proposed and compared to state‐of‐the‐art multi‐component MRF approaches in simulations and brain MRF scans of 11 healthy volunteers.ResultsSimulations and in vivo measurements show reduced noise in the estimated tissue fraction maps compared to previously proposed methods. Applying the proposed algorithm to the brain data resulted in 4 or 5 components, which could be attributed to different brain structures, consistent with previous multi‐component MRF publications.ConclusionsThe proposed algorithm is faster than previously proposed methods for multi‐component MRF and the simulations suggest improved accuracy and precision of the estimated weights. The results are easier to interpret compared to voxel‐wise methods, which combined with the improved speed is an important step toward clinical evaluation of multi‐component MRF.",
keywords = "joint sparsity constraint, MR fingerprinting, multi-component analysis, NNLS, partial volume effect, Sparsity Promoting Iterative Joint NNLS (SPIJN)",
author = "Martijn Nagtegaal and Peter Koken and Thomas Amthor and Mariya Doneva",
year = "2020",
doi = "10.1002/mrm.27947",
language = "English",
volume = "83",
pages = "521--534",
journal = "Magnetic Resonance in Medicine",
issn = "0740-3194",
publisher = "John Wiley & Sons",
number = "2",

}

RIS

TY - JOUR

T1 - Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting

AU - Nagtegaal, Martijn

AU - Koken, Peter

AU - Amthor, Thomas

AU - Doneva, Mariya

PY - 2020

Y1 - 2020

N2 - PurposeTo develop an efficient algorithm for multi‐component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties.MethodsDifferent tissues or components within a voxel are potentially separable in MRF because of their distinct signal evolutions. The observed signal evolution in each voxel can be described as a linear combination of the signals for each component with a non‐negative weight. An assumption that only a small number of components are present in the measured field of view is usually imposed in the interpretation of multi‐component data. In this work, a joint sparsity constraint is introduced to utilize this additional prior knowledge in the multi‐component analysis of MRF data. A new algorithm combining joint sparsity and non‐negativity constraints is proposed and compared to state‐of‐the‐art multi‐component MRF approaches in simulations and brain MRF scans of 11 healthy volunteers.ResultsSimulations and in vivo measurements show reduced noise in the estimated tissue fraction maps compared to previously proposed methods. Applying the proposed algorithm to the brain data resulted in 4 or 5 components, which could be attributed to different brain structures, consistent with previous multi‐component MRF publications.ConclusionsThe proposed algorithm is faster than previously proposed methods for multi‐component MRF and the simulations suggest improved accuracy and precision of the estimated weights. The results are easier to interpret compared to voxel‐wise methods, which combined with the improved speed is an important step toward clinical evaluation of multi‐component MRF.

AB - PurposeTo develop an efficient algorithm for multi‐component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties.MethodsDifferent tissues or components within a voxel are potentially separable in MRF because of their distinct signal evolutions. The observed signal evolution in each voxel can be described as a linear combination of the signals for each component with a non‐negative weight. An assumption that only a small number of components are present in the measured field of view is usually imposed in the interpretation of multi‐component data. In this work, a joint sparsity constraint is introduced to utilize this additional prior knowledge in the multi‐component analysis of MRF data. A new algorithm combining joint sparsity and non‐negativity constraints is proposed and compared to state‐of‐the‐art multi‐component MRF approaches in simulations and brain MRF scans of 11 healthy volunteers.ResultsSimulations and in vivo measurements show reduced noise in the estimated tissue fraction maps compared to previously proposed methods. Applying the proposed algorithm to the brain data resulted in 4 or 5 components, which could be attributed to different brain structures, consistent with previous multi‐component MRF publications.ConclusionsThe proposed algorithm is faster than previously proposed methods for multi‐component MRF and the simulations suggest improved accuracy and precision of the estimated weights. The results are easier to interpret compared to voxel‐wise methods, which combined with the improved speed is an important step toward clinical evaluation of multi‐component MRF.

KW - joint sparsity constraint

KW - MR fingerprinting

KW - multi-component analysis

KW - NNLS

KW - partial volume effect

KW - Sparsity Promoting Iterative Joint NNLS (SPIJN)

UR - http://www.scopus.com/inward/record.url?scp=85070741942&partnerID=8YFLogxK

U2 - 10.1002/mrm.27947

DO - 10.1002/mrm.27947

M3 - Article

VL - 83

SP - 521

EP - 534

JO - Magnetic Resonance in Medicine

T2 - Magnetic Resonance in Medicine

JF - Magnetic Resonance in Medicine

SN - 0740-3194

IS - 2

ER -

ID: 62750266