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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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Data-driven separation of MRI signal components for tissue characterization.

Sofie Rahbek1, Kristoffer H Madsen2, Henrik Lundell3

  • 1Department of Health Technology, Technical University of Denmark, Kgs. Lyngby 2800, Denmark.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|November 21, 2021
PubMed
Summary

A new data-driven method, monotonous slope non-negative matrix factorization (msNMF), accurately decomposes MRI signals. This technique improves quantitative tissue characterization and shows potential for applications like tumor analysis.

Keywords:
Data-driven decompositionDiffusionMagnetic resonance imagingMonotonous slopeRelaxometryTissue characterizationnon-negative matrix factorization

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Area of Science:

  • Biomedical Imaging
  • Quantitative MRI
  • Signal Processing

Background:

  • Magnetic Resonance Imaging (MRI) enables quantitative tissue characterization.
  • Accurate signal decomposition is crucial for assessing parameters like water fractions and diffusion coefficients.
  • Standard modeling approaches risk biased estimates if assumptions are not met.

Purpose of the Study:

  • Introduce a data-driven multicomponent analysis, monotonous slope non-negative matrix factorization (msNMF).
  • Tailor msNMF to extract expected data features from MR signals, avoiding biased parameter estimation.
  • Develop a robust method for MRI signal decomposition.

Main Methods:

  • Extended standard Non-negative Matrix Factorization (NMF) with monotonicity constraints on signal profiles and their derivatives.
  • Validated msNMF using simulated data.
  • Applied msNMF to ex vivo Diffusion Weighted Imaging (DWI) and in vivo relaxometry data, assessing reproducibility.

Main Results:

  • msNMF successfully recovered multi-exponential signals from simulated data, outperforming standard NMF.
  • Extracted diffusion components from DWI data correlated with tissue cell density.
  • Relaxometry analysis yielded edema water fraction (EWF) estimates highly correlated with existing literature and showed acceptable reproducibility.

Conclusions:

  • msNMF robustly separates MR signals into components related to tissue composition.
  • This method holds potential for quantitative tissue characterization, including tumor analysis.
  • msNMF offers an improved approach to MRI signal decomposition.