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Improved Myelin Water Quantification Using Spatially Regularized Non-negative Least Squares Algorithm
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DOI：10.1002/jmri.21783.png)
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Purpose: To improve the myelin water quantification in the brain in the presence of measurement noise and to increase the visibility of small focal lesions in myelin-water-fraction (MWF) maps. Materials and Methods: A spatially regularized non-negative least squares (srNNLS) algorithm was developed for robust myelin water quantification in the brain. The regularization for the conventional NNLS algorithm was expanded into the spatial domain in addition to the spectral domain. Synthetic data simulations were performed to study the effectiveness of this new algorithm. Experimental free-induction-decay measurements were obtained using a multi-gradient-echo pulse sequence and MWF maps were estimated using the srNNLS algorithm. The results were compared with other conventional methods. Results: A substantial decrease in MWF variability was observed in both simulations and experimental data when the srNNLS algorithm was applied. As a result, false lesions in the MWF maps disappeared and the visibility of small focal lesions improved greatly. On average, the contrast-to-noise ratio for focal lesions was improved by a factor of 2. Conclusion: The MWF variability due to the measurement noise can be substantially reduced and the detection of small focal lesions can be improved by using the srNNLS algorithm.
Keyword:
myelin content
myelin water fraction
multiple sclerosis
non-negative least squares
regularization
multiexponential analysis
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3.5
论文数:
10.0K
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2.0W
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