Title: Dynamic-Computed Tomography Angiography for Cerebral Vessel Templates and Segmentation Show Chinese title

Title: 动态计算机断层扫描血管造影用于脑血管模板和分割

Abstract: Background: Computed Tomography Angiography (CTA) is crucial for cerebrovascular disease diagnosis. Dynamic CTA is a type of imaging that captures temporal information about the We aim to develop and evaluate two segmentation techniques to segment vessels directly on CTA images: (1) creating and registering population-averaged vessel atlases and (2) using deep learning (DL). Methods: We retrieved 4D-CT of the head from our institutional research database, with bone and soft tissue subtracted from post-contrast images. An Advanced Normalization Tools pipeline was used to create angiographic atlases from 25 patients. Then, atlas-driven ROIs were identified by a CT attenuation threshold to generate segmentation of the arteries and veins using non-linear registration. To create DL vessel segmentations, arterial and venous structures were segmented using the MRA vessel segmentation tool, iCafe, in 29 patients. These were then used to train a DL model, with bone-in CT images as input. Multiple phase images in the 4D-CT were used to increase the training and validation dataset. Both segmentation approaches were evaluated on a test 4D-CT dataset of 11 patients which were also processed by iCafe and validated by a neuroradiologist. Specifically, branch-wise segmentation accuracy was quantified with 20 labels for arteries and one for veins. DL outperformed the atlas-based segmentation models for arteries (average modified dice coefficient (amDC) 0.856 vs. 0.324) and veins (amDC 0.743 vs. 0.495) overall. For ICAs, vertebral and basilar arteries, DL and atlas -based segmentation had an amDC of 0.913 and 0.402, respectively. The amDC for MCA-M1, PCA-P1, and ACA-A1 segments were 0.932 and 0.474, respectively. Conclusion: Angiographic CT templates are developed for the first time in literature. Using 4D-CTA enables the use of tools like iCafe, lessening the burden of manual annotation. Show Chinese abstract

Abstract: 背景：计算机断层扫描血管造影（CTA）对于脑血管疾病的诊断至关重要。动态CTA是一种能够捕捉时间信息的成像方式。我们旨在开发和评估两种分割技术，直接在CTA图像上分割血管：（1）创建和注册人群平均血管图谱，以及（2）使用深度学习（DL）。方法：我们从机构研究数据库中获取了头部的4D-CT，从对比后图像中减去了骨组织和软组织。使用高级配准工具流程从25名患者创建了血管图谱。然后，通过CT衰减阈值识别图谱驱动的感兴趣区域，以非线性配准生成动脉和静脉的分割。为了创建DL血管分割，使用MRA血管分割工具iCafe在29名患者中分割了动脉和静脉结构。然后将这些用于训练DL模型，以含骨的CT图像作为输入。4D-CT中的多相图像用于增加训练和验证数据集。两种分割方法在11名患者的测试4D-CT数据集上进行了评估，这些数据也通过iCafe处理并由神经放射科医生验证。具体来说，使用20个动脉标签和一个静脉标签量化了分支级分割精度。总体而言，DL在动脉（平均修改的Dice系数（amDC）0.856 vs. 0.324）和静脉（amDC 0.743 vs. 0.495）方面优于基于图谱的分割模型。对于ICA、椎动脉和基底动脉，DL和基于图谱的分割的amDC分别为0.913和0.402。MCA-M1、PCA-P1和ACA-A1段的amDC分别为0.932和0.474。结论：首次在文献中开发了血管CT模板。使用4D-CTA可以使用iCafe等工具，减轻手动注释的负担。