A Bayesian approach to diffusional kurtosis imaging
A Bayesian approach to diffusional kurtosis imaging
复制标题
扩散峰度成像的贝叶斯方法
DOI：
10.1002/mrm.28741
复制
发表时间：
2021
影响因子：
3.3
通讯作者：
Kato Ryoichi
中科院分区：
文献类型：
--
作者：
Umezawa Eizou;Ishihara Daichi;Kato Ryoichi
扩散峰度指标在检测病理变化方面表现出很高的性能，因此有望成为疾病的生物标志物。然而，峰度图往往是嘈杂的。地图的视觉质量对于疾病诊断至关重要，即使是在定量使用峰度时。提出了一种贝叶斯方法来减少峰度估计中固有的大的统计误差，同时保持对生物标志物的潜在应用。理论高斯先验是从使用最小二乘法（LSM）实现的第一步估计中确定的。似然函数方差由估计的残差确定。虽然所提出的方法类似于正则化LSM，但正则化参数不必人为调整。去噪和防止假收缩的度量dispersions之间的适当平衡automatically achieved.MethodsMap质量实现使用传统的和建议的方法进行了比较。使用模拟的低级别和高级别胶质瘤DWI数据集对胶质瘤级别分化进行受试者操作特征分析。所提出的方法的非劣效性进行了测试曲线下面积（AUC）。ResultsThe噪声的传统地图，更好地提出贝叶斯方法改善他们。通过所有峰度相关指标的AUC检验证实了拟定方法的非劣效性。所提出的方法也建立了几个metrics.ConclusionsThe所提出的方法改进了嘈杂的峰度图，同时保持其性能的生物标志物，而不增加数据采集要求或任意选择LSM正则化参数。该方法可以通过抑制过拟合来实现在扩散峰度成像（DKI）拟合函数中使用高阶项，从而提高DKI估计准确度。
PurposeDiffusional kurtosis metrics show high performance for detecting pathological changes and are therefore expected to be disease biomarkers. Kurtosis maps, however, tend to be noisy. The maps’ visual quality is crucial for disease diagnosis, even when kurtosis is being used quantitatively. A Bayesian method was proposed to curtail the large statistical error inherent in kurtosis estimation while maintaining potential application to biomarkers.TheoryGaussian priors are determined from first‐step estimations implemented using the least‐square method (LSM). The likelihood‐function variance is determined from the residuals of the estimation. Although the proposed approach is similar to a regularized LSM, regularization parameters do not have to be artificially adjusted. An appropriate balance between denoising and preventing false shrinkages of metric dispersions is automatically achieved.MethodsMap qualities achieved using the conventional and proposed methods were compared. The receiver‐operating characteristic analysis was performed for glioma‐grade differentiation using simulated low‐ and high‐grade glioma DWI datasets. Noninferiority of the proposed method was tested for areas under the curves (AUCs).ResultsThe noisier the conventional maps, the better the proposed Bayesian method improved them. Noninferiority of the proposed method was confirmed by AUC tests for all kurtosis‐related metrics. Superiority of the proposed method was also established for several metrics.ConclusionsThe proposed approach improved noisy kurtosis maps while maintaining their performances as biomarkers without increasing data acquisition requirements or arbitrarily choosing LSM regularization parameters. This approach may enable the use of higher‐order terms in diffusional kurtosis imaging (DKI) fitting functions by suppressing overfitting, thereby improving the DKI‐estimation accuracy.