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Publication Detail
Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity.
  • Publication Type:
    Journal article
  • Publication Sub Type:
  • Authors:
    Mohammadi S, Streubel T, Klock L, Edwards LJ, Lutti A, Pine KJ, Weber S, Scheibe P, Ziegler G, Gallinat J, Kühn S, Callaghan MF, Weiskopf N, Tabelow K
  • Publisher:
    Elsevier BV
  • Publication date:
  • Pagination:
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  • Article number:
  • Status:
  • Country:
    United States
  • Print ISSN:
  • PII:
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  • Keywords:
    error propagation, multi-parameter mapping, quantitative MRI, robust estimate, signal-to-noise ratio
Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.
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