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The tumor region\u2010of\u2010interest (ROI) were manually delineated on the <jats:italic>b<\/jats:italic>0 images. The diagnostic performance in distinguishing high\u2010 and low\u2010grade gliomas was assessed, and fitting accuracy was validated against H&amp;E\u2010stained pathology.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>T\u2010test, receiver operating curve (ROC), area under the curve (AUC) and DeLong's test were conducted. Significance considered at <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Bayesian methodology manifested increased accuracy with robust estimates in simulation (RMSE decreased by 29.6%, 40.9%, 13.6%, and STD decreased by 29.2%, 43.5%, and 24.0%, respectively for , , and  compared to NLLS), indicating fewer outliers and reduced error. Diagnostic performance for tumor grade was similar in both methods, however, Bayesian method generated smoother microstructural maps (outliers ratio decreased by 45.3%\u2009\u00b1\u200919.4%) and a marginal enhancement in correlation with H&amp;E staining result (<jats:italic>r<\/jats:italic>\u2009=\u20090.721 for  compared to <jats:italic>r<\/jats:italic>\u2009=\u20090.698 using NLLS, <jats:italic>P<\/jats:italic>\u2009=\u20090.5764).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>The proposed Bayesian method substantially enhances the accuracy and robustness of IMPULSED model estimation, suggesting its potential clinical utility in characterizing cellular microstructure.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 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