期刊
NEUROIMAGE
卷 262, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2022.119529
关键词
Multi-parameter mapping; Quantitative MRI; Error propagation; Signal-to-noise ratio; Robust estimate
资金
- ERA-NET NEURON (hMRIofSCI)
- Federal Ministry of Education and Research (BMBF) [01EW1711A, 01EW1711B]
- German Research Foundation (DFG) [MO 2397/5-1, MO 2397/4-1]
- Forschungszentrums Medizintechnik Hamburg (fmthh) [01fmthh2017]
- European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC [616905]
- European Union's Horizon 2020 research and innovation programme [681094]
- Swiss State Secretariat for Education, Research and Innovation (SERI) [15.0137]
- Max Planck Society
- German Research Foundation [TRR 169/C8, SFB 936/C7]
- European Union [ERC-2016StG-Self-Control-677804]
- MRC
- Spinal Research Charity through the ERA-NET Neuron joint call [MR/R000050/1]
- Wellcome [203147/Z/16/Z]
- Swiss National Science Foundation [320030_184784]
- Fondation ROGER DE SPOELBERCH
This study introduces a new method to estimate noise and artefacts in neuroimaging, improving the accuracy of Multi-Parameter Mapping (MPM). They found that the model-based signal-to-noise ratio (mSNR) is linearly correlated with the raw image-based SNR, and that mSNR varies with MPM protocols, magnetic field strength, and MPM parameters. They also generated robust MPM parameters by accounting for measurement errors, reducing variability at the group level as compared to single-repeat or averaged counterparts.
Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R-1 and R*(2), 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, R-1, 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 R-1 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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