Ruisheng Su, P Matthijs van der Sluijs, Flavius-Gabriel Marc, Frank Te Nijenhuis, Sandra A P Cornelissen, Bob Roozenbeek, Wim H van Zwam, Aad van der Lugt, Danny Ruijters, Josien Pluim, Theo van Walsum
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To facilitate fast and reproducible assessment of cerebral perfusion, this work aims to develop and validate a fully automatic and quantitative framework for perfusion DSA.</p><p><strong>Methods: </strong>We put forward a framework, perfDSA, that automatically generates deconvolution-based perfusion parametric images from cerebral DSA. It automatically extracts the arterial input function from the supraclinoid internal carotid artery (ICA) and computes deconvolution-based perfusion parametric images including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and Tmax.</p><p><strong>Results: </strong>On a DSA dataset with 1006 patients from the multicenter MR CLEAN registry, the proposed perfDSA achieves a Dice of 0.73(±0.21) in segmenting the supraclinoid ICA, resulting in high accuracy of arterial input function (AIF) curves similar to manual extraction. Moreover, some extracted perfusion images show statistically significant associations (P=2.62e <math><mo>-</mo></math> 5) with favorable functional outcomes in stroke patients.</p><p><strong>Conclusion: </strong>The proposed perfDSA framework promises to aid therapeutic decision-making in cerebrovascular interventions and facilitate discoveries of novel quantitative biomarkers in clinical practice. 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引用次数: 0
摘要
目的:脑数字减影血管造影(DSA)具有高时空分辨率,是图像引导干预中脑血流可视化和治疗指导的标准成像技术。迄今为止,DSA的脑灌注特征主要由介入医师进行视觉评估,这是耗时、容易出错和主观的。为了促进脑灌注的快速和可重复性评估,本工作旨在开发和验证一个全自动和定量的灌注DSA框架。方法:我们提出了一个框架,perfDSA,自动生成基于反卷积的脑DSA灌注参数图像。它自动提取颈动脉上颈动脉(ICA)的动脉输入函数,计算基于反卷积的灌注参数图像,包括脑血容量(CBV)、脑血流量(CBF)、平均传递时间(MTT)和Tmax。结果:在来自多中心MR CLEAN注册表的1006例患者的DSA数据集上,所提出的perfDSA在分割linoid上ICA方面达到了0.73(±0.21)的Dice,导致动脉输入函数(AIF)曲线的准确性与人工提取相似。此外,一些提取的灌注图像与脑卒中患者良好的功能预后有统计学意义(P=2.62 2e - 5)。结论:提出的perfDSA框架有望帮助脑血管干预的治疗决策,并促进在临床实践中发现新的定量生物标志物。代码可在https://github.com/RuishengSu/perfDSA上获得。
perfDSA: Automatic Perfusion Imaging in Cerebral Digital Subtraction Angiography.
Purpose: Cerebral digital subtraction angiography (DSA) is a standard imaging technique in image-guided interventions for visualizing cerebral blood flow and therapeutic guidance thanks to its high spatio-temporal resolution. To date, cerebral perfusion characteristics in DSA are primarily assessed visually by interventionists, which is time-consuming, error-prone, and subjective. To facilitate fast and reproducible assessment of cerebral perfusion, this work aims to develop and validate a fully automatic and quantitative framework for perfusion DSA.
Methods: We put forward a framework, perfDSA, that automatically generates deconvolution-based perfusion parametric images from cerebral DSA. It automatically extracts the arterial input function from the supraclinoid internal carotid artery (ICA) and computes deconvolution-based perfusion parametric images including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and Tmax.
Results: On a DSA dataset with 1006 patients from the multicenter MR CLEAN registry, the proposed perfDSA achieves a Dice of 0.73(±0.21) in segmenting the supraclinoid ICA, resulting in high accuracy of arterial input function (AIF) curves similar to manual extraction. Moreover, some extracted perfusion images show statistically significant associations (P=2.62e 5) with favorable functional outcomes in stroke patients.
Conclusion: The proposed perfDSA framework promises to aid therapeutic decision-making in cerebrovascular interventions and facilitate discoveries of novel quantitative biomarkers in clinical practice. The code is available at https://github.com/RuishengSu/perfDSA .
期刊介绍:
The International Journal for Computer Assisted Radiology and Surgery (IJCARS) is a peer-reviewed journal that provides a platform for closing the gap between medical and technical disciplines, and encourages interdisciplinary research and development activities in an international environment.