• Quan Chen, Huajun She, and Yiping P Du.
• Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
• J Magn Reson Imaging. 2020 Jul 1; 52 (1): 146-158.

BackgroundMyelin water fraction (MWF) can be quantified with analysis of the T2 * distribution, whereas deducing the T2 * spectrum from several echoes is an underdetermined and ill-posed problem.PurposeTo improve the quantification of myelin water content by using nonnegative jointly sparse (NNJS) optimization.Study TypeProspective.SubjectsNine healthy subjects.Field Strength/Sequence3T, multiecho gradient echo.AssessmentThe results of NNJS were compared with that of the nonnegative least square (NNLS)-based algorithms. Simulated models with varied MWF at different noise levels were used to evaluate the accuracy of estimations. In human data, the MWF values of different regions were compared with previous studies and the coefficient of variation (COV) was used to assess the spatial coherence.Statistical TestPaired t-test.ResultsIn simulation, the relative errors of MWF obtained from synthesized data with signal-to-noise ratio (SNR) at 500, 200, 150, and 100 were 0.08, 0.09, 0.10, and 0.12 for NNJS, 0.29, 0.43, 0.48, and 0.53 for regularized NNLS (rNNLS), and 0.19, 0.24, 0.25, and 0.26 for spatially-regularized NNLS (srNNLS). In human data, the mean values of MWF produced by NNJS in different regions were consistent with previous studies. Compared with the NNLS-based algorithms, lower COVs generated by NNJS were observed in genu, forceps minor, forceps major, and internal capsule, which were 0.44 ± 0.08, 0.48 ± 0.07, 0.46 ± 0.03, and 0.48 ± 0.09 in NNJS, 0.88 ± 0.28, 0.96 ± 0.18, 0.72 ± 0.03, and 0.85 ± 0.15 in rNNLS, and 0.56 ± 0.17, 0.64 ± 0.14, 0.50 ± 0.04 and 0.58 ± 0.13 in srNNLS.Data ConclusionQuantitative results of both simulated and human data show that NNJS provides more plausible estimation than the NNLS-based algorithms. Visual advantages of NNJS in spatial consistency can be confirmed by the comparative COV index. The proposed algorithm might improve the quantification of myelin water content.Level Of Evidence2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;52:146-158.© 2019 International Society for Magnetic Resonance in Medicine.

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