Related Experiment Video
Updated: Mar 31, 2026
Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
Published on: June 13, 2025
Noise robust spatially regularized myelin water fraction mapping with the intrinsic B1-error correction based on the
Dushyant Kumar1,2, Susanne Siemonsen1,2, Christoph Heesen2,3
1Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Purpose:
To improve the quantification accuracy of transverse relaxometry by accounting for B1 -error, after minimizing slice profile imperfections.
Materials And Methods:
The slice profile of refocusing pulses was optimized by setting refocusing slice thicknesses three times that of the excitation pulse. The first step of data processing combined the L-curve approach with the linearized version of the extended phase graph model to jointly estimate the temporal regularization constant map and the flip angle error (FAE)-map. The second step improved the noise robustness of the reconstruction by imposing a spatial smoothness constraint on T2 -distributions. The proposed method (spatial-regularization-with-FAE-correction) was evaluated against methods without FAE-correction (conventional-regularization-without-FAE-correction, spatial-regularization-without-FAE-correction) and conventional-regularization-with-FAE-correction using relevant statistics (simulated data: mean square myelin reconstruction error [MSMRE] and averaged-symmetric-Kullbeck-Leibler score [SKL] between returned distributions and ground truths; experimental data: median of mean square error [MMSE] of fitting across entire data-set and coefficient of variation [COV] in white-matter [WM] regions of interest [ROIs]).
Results:
In simulation, our method resulted in reduced MSMRE (at signal-to-noise ratio [SNR] = 200: MSMRESpatial-regularization-without-FAEC = 0.057; MSMRESpatial-regularization-with-FAEC = 0.0107) and reduced SKL scores (at SNR = 200: SKLSpatial-regularization-without-FAEC = 0.061; SKLSpatial-regularization-with-FAEC = 0.0143). In human volunteers, our method yielded a reduced MSE of fitting (MMSESpatial-regularization-without-FAEC = (2.26 ± 0.60) × 10(-3) ; MMSESpatial-regularization-with-FAEC = (1.57 ± 0.44) × 10(-4) )and also resulted in reduced COV (COVSpatial-regularization-without-FAEC = 0.08-0.19; COVSpatial-regularization-with-FAEC = 0.09-0.12). In a water-phantom, a good correlation between the absolute value of measured B1 -map and FAE-map was found (regression analysis: slope = 1.04; R(2) = 0.66).
Conclusion:
The proposed method resulted in more accurate and noise robust myelin water fraction maps with improved depiction of subcortical WM structures.
