Noise robust spatially regularized myelin water fraction mapping with the intrinsic B1-error correction based on the linearized version of the extended phase graph model

TY - JOUR

T1 - Noise robust spatially regularized myelin water fraction mapping with the intrinsic B1-error correction based on the linearized version of the extended phase graph model

AU - Kumar, Dushyant

AU - Siemonsen, Susanne

AU - Heesen, Christoph

AU - Fiehler, Jens

AU - Sedlacik, Jan

N1 - © 2015 Wiley Periodicals, Inc.

PY - 2016/4/1

Y1 - 2016/4/1

N2 - 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. J. Magn. Reson. Imaging 2015.

AB - 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. J. Magn. Reson. Imaging 2015.

U2 - 10.1002/jmri.25078

DO - 10.1002/jmri.25078

M3 - SCORING: Journal article

C2 - 26477610

VL - 43

SP - 800

EP - 817

JO - J MAGN RESON IMAGING

JF - J MAGN RESON IMAGING

SN - 1053-1807

IS - 4

ER -