Relationship between kurtosis and bi-exponential characterization of high b-value diffusion-weighted imaging: application to prostate cancer
Relationship between kurtosis and bi-exponential characterization of high b-value diffusion-weighted imaging: application to prostate cancer
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DOI：
10.1177/0284185118770889
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发表时间：
2018-12-01
期刊：
影响因子：
1.3
通讯作者：
White, Nathan S.
中科院分区：
文献类型：
--
作者：
Karunamuni, Roshan A.;Kuperman, Joshua;White, Nathan S.
背景：高b值弥散加权成像在多部位肿瘤组织的检测中有应用价值。扩散峰度和双指数模型是两种流行的基于模型的技术，它们之间的相互关系尚未得到充分的探讨。目的：确定在检测可疑前列腺病变时，过度峰度与双指数模型中信号分量之间的关系。材料和方法：这项回顾研究分析了12例正常前列腺组织或13例可疑病变(n=13，每个患者1个病变)的患者，这些患者的临床护理包括高b值扩散序列。观测到的信号强度采用双指数衰减模型，由此推导出慢速运动分量的信号分数(SFS)。此外，使用信号分数和两个指数的ADC(KCOMP)来计算超额峰度。作为比较，还使用扩散信号的累积量展开(KCE)计算峰度。结果：在前列腺常见的SFS范围内，K和KCE均随SFS的增加而增加。SFS、KCE和KCOMP在区分可疑病变和正常前列腺组织方面的体素接收器操作特征性能分别为0.86(95%可信区间[CI]=0.85~0.87)、0.69(95%CI=0.68~0.70)和0.86(95%CI=0.86~0.87)。结论：在双成分扩散环境中，KCOMP是SFS的一个标度值，因此能够以同样的精度区分可疑病变。KCE提供了一种计算成本较低的峰度近似值，但不提供与SFS和KCOMP相同的区分能力。
Background: High b-value diffusion-weighted imaging has application in the detection of cancerous tissue across multiple body sites. Diffusional kurtosis and bi-exponential modeling are two popular model-based techniques, whose performance in relation to each other has yet to be fully explored.Purpose: To determine the relationship between excess kurtosis and signal fractions derived from bi-exponential modeling in the detection of suspicious prostate lesions.Material and Methods: This retrospective study analyzed patients with normal prostate tissue (n = 12) or suspicious lesions (n = 13, one lesion per patient), as determined by a radiologist whose clinical care included a high b-value diffusion series. The observed signal intensity was modeled using a bi-exponential decay, from which the signal fraction of the slow-moving component was derived (SFs). In addition, the excess kurtosis was calculated using the signal fractions and ADCs of the two exponentials (KCOMP). As a comparison, the kurtosis was also calculated using the cumulant expansion for the diffusion signal (KCE).Results: Both K and KCE were found to increase with SFs within the range of SFs commonly found within the prostate. Voxel-wise receiver operating characteristic performance of SFs, KCE, and KCOMP in discriminating between suspicious lesions and normal prostate tissue was 0.86 (95% confidence interval [CI] = 0.85 - 0.87), 0.69 (95% CI = 0.68-0.70), and 0.86 (95% CI= 0.86-0.87), respectively.Conclusion: In a two-component diffusion environment, KCOMP is a scaled value of SFs and is thus able to discriminate suspicious lesions with equal precision. KCE provides a computationally inexpensive approximation of kurtosis but does not provide the same discriminatory abilities as SFs and KCOMP.