Karunamuni Roshan A, Kuperman Joshua, Seibert Tyler M, Schenker Natalie, Rakow-Penner Rebecca, Sundar V S, Teruel Jose R, Goa Pal E, Karow David S, Dale Anders M, White Nathan S
1 Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
2 Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Acta Radiol. 2018 Dec;59(12):1523-1529. doi: 10.1177/0284185118770889. Epub 2018 Apr 17.
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.
To determine the relationship between excess kurtosis and signal fractions derived from bi-exponential modeling in the detection of suspicious prostate lesions.
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).
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.
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.
高b值扩散加权成像在多个身体部位的癌组织检测中具有应用价值。扩散峰度和双指数模型是两种常用的基于模型的技术，它们之间的性能关系尚未得到充分探索。
确定在可疑前列腺病变检测中，双指数模型得出的峰度和信号分数之间的关系。
本回顾性研究分析了前列腺组织正常（n = 12）或有可疑病变（n = 13，每位患者一个病变）的患者，由一位临床诊疗包括高b值扩散序列的放射科医生确定。使用双指数衰减对观察到的信号强度进行建模，由此得出慢扩散成分的信号分数（SFs）。此外，使用两个指数的信号分数和表观扩散系数计算超额峰度（KCOMP）。作为比较，还使用扩散信号的累积量展开计算峰度（KCE）。
在前列腺常见的SFs范围内，K和KCE均随SFs增加。SFs、KCE和KCOMP在区分可疑病变和正常前列腺组织时的体素级接收器操作特征性能分别为0.86（95%置信区间[CI] = 0.85 - 0.87）、0.69（95% CI = 0.68 - 0.70）和0.86（95% CI = 0.86 - 0.87）。
在双成分扩散环境中，KCOMP是SFs的标度值，并因此能够以相同精度区分可疑病变。KCE提供了一种计算成本较低的峰度近似值，但不具备与SFs和KCOMP相同的鉴别能力。
