Liu Kuiyuan, Lin Zixuan, Zheng Tianshu, Ba Ruicheng, Zhang Zelin, Li Haotian, Zhang Hongxi, Tal Assaf, Wu Dan
Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
J Magn Reson Imaging. 2025 Feb;61(2):724-734. doi: 10.1002/jmri.29434. Epub 2024 May 20.
Accurately fitting diffusion-time-dependent diffusion MRI (t-dMRI) models poses challenges due to complex and nonlinear formulas, signal noise, and limited clinical data acquisition.
Introduce a Bayesian methodology to refine microstructural fitting within the IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) model and optimize the prior distribution within the Bayesian framework.
Retrospective.
Involving 69 pediatric patients (median age 6 years, interquartile range [IQR] 3-9 years, 61% male) with 41 low-grade and 28 high-grade gliomas, of which 76.8% were identified within the brainstem or cerebellum.
FIELD STRENGTH/SEQUENCE: 3 T, oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE).
The Bayesian method's performance in fitting cell diameter ( ), intracellular volume fraction ( ), and extracellular diffusion coefficient ( ) was compared against the NLLS method, considering simulated and experimental data. The tumor region-of-interest (ROI) were manually delineated on the b0 images. The diagnostic performance in distinguishing high- and low-grade gliomas was assessed, and fitting accuracy was validated against H&E-stained pathology.
T-test, receiver operating curve (ROC), area under the curve (AUC) and DeLong's test were conducted. Significance considered at P < 0.05.
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% ± 19.4%) and a marginal enhancement in correlation with H&E staining result (r = 0.721 for compared to r = 0.698 using NLLS, P = 0.5764).
The proposed Bayesian method substantially enhances the accuracy and robustness of IMPULSED model estimation, suggesting its potential clinical utility in characterizing cellular microstructure.
3 TECHNICAL EFFICACY: Stage 1.
由于公式复杂且非线性、信号噪声以及临床数据采集有限，准确拟合扩散时间依赖的扩散磁共振成像（t-dMRI）模型具有挑战性。
引入一种贝叶斯方法来优化IMPULSED（使用有限频谱编辑扩散成像微观结构参数）模型中的微观结构拟合，并在贝叶斯框架内优化先验分布。
回顾性研究。
涉及69名儿科患者（中位年龄6岁，四分位间距[IQR]为3 - 9岁，61%为男性），患有41例低级别和28例高级别胶质瘤，其中76.8%位于脑干或小脑。
场强/序列：3T，振荡梯度自旋回波（OGSE）和脉冲梯度自旋回波（PGSE）。
考虑模拟数据和实验数据，将贝叶斯方法在拟合细胞直径（ ）、细胞内体积分数（ ）和细胞外扩散系数（ ）方面的性能与非线性最小二乘法（NLLS）进行比较。在b0图像上手动勾勒肿瘤感兴趣区域（ROI）。评估区分高级别和低级别胶质瘤的诊断性能，并根据苏木精和伊红（H&E）染色病理结果验证拟合准确性。
进行t检验、受试者操作特征曲线（ROC）、曲线下面积（AUC）和德龙检验。P < 0.05时认为具有统计学意义。
贝叶斯方法在模拟中表现出更高的准确性和稳健估计（与NLLS相比， 、 和 的均方根误差[RMSE]分别降低了29.
