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评估椎体骨髓内的体素内不相干运动(IVIM)参数:五种算法的比较研究。

Estimation of intravoxel incoherent motion (IVIM) parameters in vertebral bone marrow: a comparative study of five algorithms.

机构信息

Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China.

College of Information Science and Technology, Nanjing Forestry University, Nanjing, 210037, China.

出版信息

MAGMA. 2023 Oct;36(5):837-847. doi: 10.1007/s10334-023-01064-4. Epub 2023 Jan 30.

Abstract

OBJECTIVES

To access the performances of different algorithms for quantification of Intravoxel incoherent motion (IVIM) parameters D, f, [Formula: see text] in Vertebral Bone Marrow (VBM).

MATERIALS AND METHODS

Five algorithms were studied: four deterministic algorithms (the One-Step and three segmented methods: Two-Step, Three-Step, and Fixed-[Formula: see text] algorithm) based on the least-squares (LSQ) method and a Bayesian probabilistic algorithm. Numerical simulations and quantification of IVIM parameters D, f, [Formula: see text] in vivo in vertebral bone marrow, were done on six healthy volunteers. The One-way repeated-measures analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test (p value = 0.05) was applied.

RESULTS

In numerical simulations, the Bayesian algorithm provided the best estimation of D, f, [Formula: see text] compared to the deterministic algorithms. In vivo VBM-IVIM, the values of D and f estimated by the Bayesian algorithm were close to those of the One-Step method, in contrast to the three segmented methods.

DISCUSSION

The comparison of the five algorithms indicates that the Bayesian algorithm provides the best estimation of VBM-IVIM parameters, in both numerical simulations and in vivo data.

摘要

目的

评估不同算法在定量椎体骨髓内体素内不相干运动(IVIM)参数 D、f、[公式:见正文]中的表现。

材料与方法

研究了 5 种算法:4 种基于最小二乘法(LSQ)的确定性算法(一步法和三种分段方法:二步法、三步法和固定-[公式:见正文]算法)和一种贝叶斯概率算法。在 6 名健康志愿者的椎体骨髓内进行了 IVIM 参数 D、f、[公式:见正文]的数值模拟和定量研究。采用单向重复测量方差分析(ANOVA),并进行了 Bonferroni 多重比较检验(p 值=0.05)。

结果

在数值模拟中,与确定性算法相比,贝叶斯算法提供了 D、f、[公式:见正文]的最佳估计。在 vivo VBM-IVIM 中,贝叶斯算法估计的 D 和 f 值接近一步法,而与三种分段方法相反。

讨论

5 种算法的比较表明,贝叶斯算法在数值模拟和体内数据中均能提供 VBM-IVIM 参数的最佳估计。

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