Wang Cheng, Chen Siqi, Mi Donghua
Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Front Neurol. 2024 Jan 18;14:1328184. doi: 10.3389/fneur.2023.1328184. eCollection 2023.
Current clinical computed tomography arteriography (cCTA) and clinical computed tomography venography (cCTV) images often display restricted cerebrovascular profiles, incomplete brain tissue segmentation, and incomplete artery-vein segmentation. Especially for vessels associated with diseases, capturing their complete profiles proves challenging.
In this work, we developed a Task-driven Cerebral Angiographic Imaging (TDCAI) technique using computed tomography perfusion (CTP) images of stroke patients. A evaluation on intracranial hemorrhagic stroke (IHS) and acute ischemic stroke (AIS) cases was performed with CT perfusion imaging. The TDCAI technique processed the CTP images, resulting in supplementary diagnostic images, including CTA, CTV, centerline images of the vessels-of-interest [internal carotid artery (ICA) for AIS patients, Labbé vein for IHS patients], and straightened images of the vessels-of-interest.
We conducted a comparison between the obtained CTA/CTV images and the cCTA/cCTV images in terms of overall image quality and visibility of the vessels-of-interest. By constructing a virtual vascular phantom, we extracted its centerline and compared it with the actual centerline to calculate maximum and average deviations. This allowed us to evaluate both the accuracy of the centerline extraction algorithm and its capability to resist the influence of side branches. We assessed whether vascular stenosis and dilatation could be expressed in straightened vessel images, conducting statistical analyses to establish the superiority of TDCAI technique.
This study proposes a TDCAI technique to eliminate bone and soft tissue interference, effectively segregate the comprehensive cerebral venous and arterial systems, and extract centerlines and straighten the vessels-of-interest, which would aid doctors in assessing the outflow profiles of vessels after a stroke and seeking imaging biomarkers correlated with clinical outcomes.
当前临床计算机断层血管造影（cCTA）和临床计算机断层静脉造影（cCTV）图像常常显示脑血管轮廓受限、脑组织分割不完整以及动静脉分割不完整。尤其是对于与疾病相关的血管，获取其完整轮廓具有挑战性。
在本研究中，我们利用中风患者的计算机断层灌注（CTP）图像开发了一种任务驱动的脑血管造影成像（TDCAI）技术。对颅内出血性中风（IHS）和急性缺血性中风（AIS）病例进行了CT灌注成像评估。TDCAI技术对CTP图像进行处理，生成辅助诊断图像，包括CTA、CTV、感兴趣血管的中心线图像（AIS患者的颈内动脉[ICA]，IHS患者的Labbe静脉）以及感兴趣血管的拉直图像。
我们在整体图像质量和感兴趣血管的可视性方面，对获得的CTA/CTV图像与cCTA/cCTV图像进行了比较。通过构建虚拟血管模型，提取其中心线并与实际中心线进行比较，以计算最大偏差和平均偏差。这使我们能够评估中心线提取算法的准确性及其抵抗侧支影响的能力。我们评估了血管狭窄和扩张是否能在拉直的血管图像中体现，并进行统计分析以确立TDCAI技术的优势。
本研究提出了一种TDCAI技术，以消除骨骼和软组织干扰，有效分离完整的脑静脉和动脉系统，提取中心线并拉直感兴趣血管，这将有助于医生评估中风后血管的流出轮廓，并寻找与临床结果相关的影像学生物标志物。
