TY - JOUR
T1 - Data-driven separation of MRI signal components for tissue characterization
AU - Rahbek, Sofie
AU - Madsen, Kristoffer H
AU - Lundell, Henrik
AU - Mahmood, Faisal
AU - Hanson, Lars G
N1 - Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.
PY - 2021/12
Y1 - 2021/12
N2 - PURPOSE: MRI can be utilized for quantitative characterization of tissue. To assess e.g. water fractions or diffusion coefficients for compartments in the brain, a decomposition of the signal is necessary. Imposing standard models carries the risk of estimating biased parameters if model assumptions are violated. This work introduces a data-driven multicomponent analysis, the monotonous slope non-negative matrix factorization (msNMF), tailored to extract data features expected in MR signals.METHODS: The msNMF was implemented by extending the standard NMF with monotonicity constraints on the signal profiles and their first derivatives. The method was validated using simulated data, and subsequently applied to both ex vivo DWI data and in vivo relaxometry data. Reproducibility of the method was tested using the latter.RESULTS: The msNMF recovered the multi-exponential signals in the simulated data and showed superiority to standard NMF (based on the explained variance, area under the ROC curve, and coefficient of variation). Diffusion components extracted from the DWI data reflected the cell density of the underlying tissue. The relaxometry analysis resulted in estimates of edema water fractions (EWF) highly correlated with published results, and demonstrated acceptable reproducibility.CONCLUSION: The msNMF can robustly separate MR signals into components with relation to the underlying tissue composition, and may potentially be useful for e.g. tumor tissue characterization.
AB - PURPOSE: MRI can be utilized for quantitative characterization of tissue. To assess e.g. water fractions or diffusion coefficients for compartments in the brain, a decomposition of the signal is necessary. Imposing standard models carries the risk of estimating biased parameters if model assumptions are violated. This work introduces a data-driven multicomponent analysis, the monotonous slope non-negative matrix factorization (msNMF), tailored to extract data features expected in MR signals.METHODS: The msNMF was implemented by extending the standard NMF with monotonicity constraints on the signal profiles and their first derivatives. The method was validated using simulated data, and subsequently applied to both ex vivo DWI data and in vivo relaxometry data. Reproducibility of the method was tested using the latter.RESULTS: The msNMF recovered the multi-exponential signals in the simulated data and showed superiority to standard NMF (based on the explained variance, area under the ROC curve, and coefficient of variation). Diffusion components extracted from the DWI data reflected the cell density of the underlying tissue. The relaxometry analysis resulted in estimates of edema water fractions (EWF) highly correlated with published results, and demonstrated acceptable reproducibility.CONCLUSION: The msNMF can robustly separate MR signals into components with relation to the underlying tissue composition, and may potentially be useful for e.g. tumor tissue characterization.
KW - Data-driven decomposition
KW - Diffusion
KW - Magnetic resonance imaging
KW - Monotonous slope
KW - non-negative matrix factorization
KW - Relaxometry
KW - Tissue characterization
UR - http://www.scopus.com/inward/record.url?scp=85119267409&partnerID=8YFLogxK
U2 - 10.1016/j.jmr.2021.107103
DO - 10.1016/j.jmr.2021.107103
M3 - Journal article
C2 - 34801822
VL - 333
SP - 1
EP - 11
JO - Journal of Magnetic Resonance
JF - Journal of Magnetic Resonance
SN - 1090-7807
M1 - 107103
ER -