Abstract
PurposeMRI 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. MethodsThe 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. ResultsThe 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. ConclusionThe 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.
Highlights
Quantitative assessment of tissue properties has become possible with specific MRI sequences that enable extraction of parameters such as T1 and edema water fraction
We introduce and demonstrate the monotonous slope non-negative matrix factorization (msNMF) for multicomponent analysis of MRI data, and show examples where the estimated components are related to the underlying tissue structure, demonstrating the use of the method as a robust alternative to advanced modeling
The true exponential signals and true mixture maps are included for visual comparison, and the quantitative comparison revealed EVW 1⁄4 0:984 and AUCH 1⁄4 0:999
Summary
Quantitative assessment of tissue properties has become possible with specific MRI sequences that enable extraction of parameters such as T1 and edema water fraction. With a quantitative characterization of the tissue, it is possible to compare data across scans and subjects, and to capture changes at sub-voxel scale [1,2] This can potentially improve our understanding of pathophysiology and generate biomarkers for e.g. abnormality detection, disease staging or treatment response assessment. A voxel can contain multiple tissue compartments (partial volume effect) or orientational dispersion of anisotropic domains, and the diffusion can be affected by water exchange between compartments [5,6]. Biophysical processes such as flow and perfusion can cause extra signal decay [7,8].
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