Fakhari Nikan, Aguet Julien, Nguyen Minh B, Zhang Naiyuan, Mertens Luc, Jain Amish, Sled John G, Villemain Olivier, Baranger Jérôme
Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada.
Translational Medicine, The Hospital for Sick Children Research Institute, Toronto, Ontario, M5G 0A4, Canada.
Phys Med Biol. 2024 Dec 6;69(24). doi: 10.1088/1361-6560/ad94ca.
Cerebral arterial and venous flow (A/V) classification is a key parameter for understanding dynamic changes in neonatal brain perfusion. Currently, transfontanellar ultrasound Doppler imaging is the reference clinical technique able to discriminate between A/V using vascular indices such as resistivity index (RI) or pulsatility index (PI). However, under conditions of slow arterial and venular flow, small signal fluctuations can lead to potential misclassifications of vessels. Recently, ultrafast ultrasound imaging has paved the way for better sensitivity and spatial resolution. Here, we show that A/V classification can be performed robustly using ultrafast Doppler spectrogram.The overall classification steps are as follows: for any pixel within a vessel, a normalized Doppler spectrogram (NDS) is computed that allows for normalized correlation analysis with ground-truth signals that were established semi-automatically based on anatomical/physiological references. Furthermore, A/V classification is performed by computing Pearson correlation coefficient between NDS in ground-truth domains and the individual pixel's NDS inside vessels and finding an optimal threshold.When applied to human newborns (= 40), the overall accuracy, sensitivity, and specificity were found to be 88.5% ± 6.7%, 88.5% ± 6.5%, and 87.0% ± 8.8% respectively. We also examined strategies to fully automate this process, leading to a moderate decrease of 1%-3% in the same metrics. Additionally, when compared to the main clinical metrics such as RI, and PI, the receiver operating characteristic curves exhibited higher areas under the curve; on average by +36% (< 0.0001) in the full imaging sector, +35% (= 0.0116) in the cortical regions, +53% (< 0.0001) in the basal ganglia, +28% (= 0.0051) in the cingulate gyrus, and +35% (< 0.0001) in the remaining brain structures.Our findings suggest that the proposed NDS-based approach can distinguish between A/V when studying cerebral perfusion in neonates.
脑动静脉血流(A/V)分类是了解新生儿脑灌注动态变化的关键参数。目前,经囟门超声多普勒成像技术是能够利用血管指数(如阻力指数(RI)或搏动指数(PI))区分动静脉的参考临床技术。然而,在动脉和静脉血流缓慢的情况下,小的信号波动可能导致血管的潜在误分类。最近,超快超声成像为提高灵敏度和空间分辨率铺平了道路。在此,我们表明使用超快多普勒频谱图可以稳健地进行A/V分类。总体分类步骤如下:对于血管内的任何像素,计算归一化多普勒频谱图(NDS),该图允许与基于解剖学/生理学参考半自动建立的真实信号进行归一化相关分析。此外,通过计算真实域中的NDS与血管内单个像素的NDS之间的皮尔逊相关系数并找到最佳阈值来进行A/V分类。当应用于40例人类新生儿时,总体准确率、灵敏度和特异性分别为88.5%±6.7%、88.5%±6.5%和87.0%±8.8%。我们还研究了使这一过程完全自动化的策略,导致相同指标适度下降1%-3%。此外,与主要临床指标如RI和PI相比,接收器操作特征曲线在曲线下具有更高的面积;在整个成像区域平均提高36%(<0.0001),在皮质区域提高35%(=0.0116),在基底神经节提高53%(<0.0001),在扣带回提高28%(=0.0051),在其余脑结构提高35%(<0.0001)。我们的研究结果表明,所提出的基于NDS的方法在研究新生儿脑灌注时能够区分动静脉。