Abstract：
Time-of-flight magnetic resonance angiography (TOF-MRA) is a non-contrast technique used to visualize neurovascular. However, manual reconstruction of the volume render (VR) by radiologists is time-consuming and labor-intensive. Deep learning-based (DL-based) vessel segmentation technology may provide intelligent automation workflow. To evaluate the image quality of DL vessel segmentation for automatically acquiring intracranial arteries in TOF-MRA. A total of 394 TOF-MRA scans were selected, which included cerebral vascular health, aneurysms, or stenoses. Both our proposed method and two state-of-the-art DL methods are evaluated on external datasets for generalization ability. For qualitative assessment, two experienced clinical radiologists evaluated the image quality of cerebrovascular diagnostic and visualization (scoring 0-5 as unacceptable to excellent) obtained by manual VR reconstruction or automatic convolutional neural network (CNN) segmentation. The proposed CNN outperforms the other two DL-based methods in clinical scoring on external datasets, and its visualization was evaluated by readers as having the appearance of the radiologists\' manual reconstructions. Scoring of proposed CNN and VR of intracranial arteries demonstrated good to excellent agreement with no significant differences (median, 5.0 and 5.0, P ≥ 12) at healthy-type scans. All proposed CNN image quality were considered to have adequate diagnostic quality (median scores > 2). Quantitative analysis demonstrated a superior dice similarity coefficient of cerebrovascular overlap (training sets and validation sets; 0.947 and 0.927). Automatic cerebrovascular segmentation using DL is feasible and the image quality in terms of vessel integrity, collateral circulation and lesion morphology is comparable to expert manual VR without significant differences.
摘要：
飞行时间磁共振血管造影（TOF-MRA）是一种用于可视化神经血管的非对比技术。然而,由放射科医师手动重建体积渲染(VR)是耗时且费力的。基于深度学习(基于DL)的血管分割技术可以提供智能自动化工作流。评价TOF-MRA中DL血管分割自动采集颅内动脉的图像质量。共选取394次TOF-MRA扫描,其中包括脑血管健康，动脉瘤,或狭窄。我们提出的方法和两种最先进的DL方法都在外部数据集上进行了泛化能力评估。对于定性评估，两名经验丰富的临床放射科医师评估了通过手动VR重建或自动卷积神经网络（CNN）分割获得的脑血管诊断和可视化图像质量（评分0-5为不可接受的优秀）。所提出的CNN在外部数据集上的临床评分方面优于其他两种基于DL的方法，它的可视化被读者评估为具有放射科医生手动重建的外观。颅内动脉的拟议CNN和VR的评分显示出良好的一致性，没有显着差异（中位数，5.0和5.0，P≥12）在健康型扫描中。所有提出的CNN图像质量被认为具有足够的诊断质量(中值分数>2)。定量分析表明，脑血管重叠的骰子相似系数（训练集和验证集；0.947和0.927）。使用DL的自动脑血管分割是可行的，并且在血管完整性方面的图像质量，侧支循环和病变形态与专家手动VR相当，无显著差异。
