Ye Chen, Xu Daoyun, Qin Yongbin, Wang Lihui, Wang Rongpin, Li Wuchao, Kuai Zixiang, Zhu Yuemin
Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China.
Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China.
Med Phys. 2020 Sep;47(9):4372-4385. doi: 10.1002/mp.14233. Epub 2020 Jul 28.
Intravoxel incoherent motion (IVIM) magnetic resonance imaging is a potential noninvasive technique for the diagnosis of brain tumors. However, perfusion-related parameter mapping is a persistent problem. The purpose of this paper is to investigate the IVIM parameter mapping of brain tumors using Bayesian fitting and low b-values.
Bayesian shrinkage prior (BSP) fitting method and different low b-value distributions were used to estimate IVIM parameters (diffusion D, pseudo-diffusion D*, and perfusion fraction F). The results were compared to those obtained by least squares (LSQ) on both simulated and in vivo brain data. Relative error (RE) and reproducibility were used to evaluate the results. The differences of IVIM parameters between brain tumor and normal regions were compared and used to assess the performance of Bayesian fitting in the IVIM application of brain tumor.
In tumor regions, the value of D* tended to be decreased when the number of low b-values was insufficient, especially with LSQ. BSP required less low b-values than LSQ for the correct estimation of perfusion parameters of brain tumors. The IVIM parameter maps of brain tumors yielded by BSP had smaller variability, lower RE, and higher reproducibility with respect to those obtained by LSQ. Obvious differences were observed between tumor and normal regions in parameters D (P < 0.05) and F (P < 0.001), especially F. BSP generated fewer outliers than LSQ, and distinguished better tumors from normal regions in parameter F.
Intravoxel incoherent motion parameters clearly allow brain tumors to be differentiated from normal regions. Bayesian fitting yields robust IVIM parameter mapping with fewer outliers and requires less low b-values than LSQ for the parameter estimation.
体素内不相干运动（IVIM）磁共振成像技术是一种潜在的用于脑肿瘤诊断的非侵入性技术。然而，灌注相关参数映射一直是个问题。本文旨在利用贝叶斯拟合和低b值研究脑肿瘤的IVIM参数映射。
采用贝叶斯收缩先验（BSP）拟合方法和不同的低b值分布来估计IVIM参数（扩散系数D、伪扩散系数D*和灌注分数F）。将结果与在模拟脑数据和体内脑数据上通过最小二乘法（LSQ）获得的结果进行比较。使用相对误差（RE）和可重复性来评估结果。比较脑肿瘤与正常区域之间IVIM参数的差异，并用于评估贝叶斯拟合在脑肿瘤IVIM应用中的性能。
在肿瘤区域，当低b值数量不足时，尤其是使用LSQ时，D*值往往会降低。对于正确估计脑肿瘤的灌注参数，BSP比LSQ需要更少的低b值。与通过LSQ获得的结果相比，BSP生成的脑肿瘤IVIM参数图具有更小的变异性、更低的RE和更高的可重复性。在参数D（P < 0.05）和F（P < 0.001），尤其是F方面，肿瘤与正常区域之间观察到明显差异。BSP产生的异常值比LSQ少，并且在参数F方面能更好地区分肿瘤与正常区域。
体素内不相干运动参数能够清晰地区分脑肿瘤与正常区域。贝叶斯拟合产生的IVIM参数映射更稳健，异常值更少，并且在参数估计时比LSQ需要更少的低b值。
