Pham S D T, Chatziantoniou C, van Vliet J T, van Tuijl R J, Bulk M, Costagli M, de Rochefort L, Kraff O, Ladd M E, Pine K, Ronen I, Siero J C W, Tosetti M, Villringer A, Biessels G J, Zwanenburg J J M
Translational Neuroimaging Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Division Imaging & Oncology, Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands.
Neuroinformatics. 2025 Jan 22;23(2):11. doi: 10.1007/s12021-024-09703-4.
Blood flow velocity in the cerebral perforating arteries can be quantified in a two-dimensional plane with phase contrast magnetic imaging (2D PC-MRI). The velocity pulsatility index (PI) can inform on the stiffness of these perforating arteries, which is related to several cerebrovascular diseases. Currently, there is no open-source analysis tool for 2D PC-MRI data from these small vessels, impeding the usage of these measurements. In this study we present the Small vessEL MArker (SELMA) analysis software as a novel, user-friendly, open-source tool for velocity analysis in cerebral perforating arteries. The implementation of the analysis algorithm in SELMA was validated against previously published data with a Bland-Altman analysis. The inter-rater reliability of SELMA was assessed on PC-MRI data of sixty participants from three MRI vendors between eight different sites. The mean velocity (v) and velocity PI of SELMA was very similar to the original results (v: mean difference ± standard deviation: 0.1 ± 0.8 cm/s; velocity PI: mean difference ± standard deviation: 0.01 ± 0.1) despite the slightly higher number of detected vessels in SELMA (N: mean difference ± standard deviation: 4 ± 9 vessels), which can be explained by the vessel selection paradigm of SELMA. The Dice Similarity Coefficient of drawn regions of interest between two operators using SELMA was 0.91 (range 0.69-0.95) and the overall intra-class coefficient for N, v, and velocity PI were 0.92, 0.84, and 0.85, respectively. The differences in the outcome measures was higher between sites than vendors, indicating the challenges in harmonizing the 2D PC-MRI sequence even across sites with the same vendor. We show that SELMA is a consistent and user-friendly analysis tool for small cerebral vessels.
脑穿支动脉的血流速度可以通过二维相位对比磁共振成像（2D PC-MRI）在二维平面上进行量化。速度搏动指数（PI）可以反映这些穿支动脉的僵硬度，而僵硬度与多种脑血管疾病相关。目前，尚无用于分析这些小血管2D PC-MRI数据的开源分析工具，这阻碍了这些测量方法的应用。在本研究中，我们展示了小血管标记（SELMA）分析软件，这是一种用于脑穿支动脉速度分析的新型、用户友好的开源工具。SELMA中分析算法的实现通过Bland-Altman分析与先前发表的数据进行了验证。在来自八个不同地点的三家MRI供应商的60名参与者的PC-MRI数据上评估了SELMA的评分者间可靠性。尽管SELMA中检测到的血管数量略多（N：平均差异±标准差：4±9条血管），这可以用SELMA的血管选择模式来解释，但其平均速度（v）和速度PI与原始结果非常相似（v：平均差异±标准差：0.1±0.8 cm/s；速度PI：平均差异±标准差：0.01±0.1）。使用SELMA的两名操作者之间绘制的感兴趣区域的Dice相似系数为0.91（范围0.69 - 0.95），N、v和速度PI的总体组内系数分别为0.92、0.84和0.85。不同地点之间结果指标的差异高于不同供应商之间，这表明即使在使用相同供应商的不同地点之间，协调2D PC-MRI序列也存在挑战。我们表明，SELMA是一种用于脑小血管的一致且用户友好的分析工具。
