背景:由于手注射的可变性,颅内动脉瘤(IA)的术中2D定量血管造影术(QA)具有准确性挑战。尽管奇异值分解(SVD)算法在减少计算机断层扫描灌注(CTP)中的偏差方面取得了成功,它们在2DQA中的应用还没有得到广泛的探索。本研究旨在通过研究基于SVD的反卷积方法在2DQA中的潜力来弥合这一差距,特别是在解决注射持续时间的可变性。
目标:建立在QA中已确定的局限性的基础上,该研究旨在将基于SVD的反卷积技术从CTP应用到IAs的QA。这种改编试图利用QA的高时间分辨率,尽管它是二维的,提高血流动力学参数评估的一致性和准确性。目标是开发一种能够可靠评估IAs血液动力学状况的方法,独立于注入变量,改善神经血管诊断。
方法:本研究包括3例颈内动脉瘤(ICA)病例。使用计算流体动力学(CFD)针对三种生理相关的入口速度生成虚拟血管造影图,以模拟造影剂注射持续时间。对入口和动脉瘤圆顶产生时间-密度曲线(TDC)。各种SVD变体,包括有和没有经典Tikhonov正则化的标准SVD(SVD),块循环SVD(bSVD),和振荡指数SVD(OSVD),应用于虚拟血管造影。该方法应用于虚拟血管造影,以恢复动脉瘤圆顶脉冲响应函数(IRF)并提取与流量相关的参数,例如峰值高度PHIRF,曲线下面积AUCIRF,和平均运输时间MTT。接下来,QA参数之间的相关性,注射持续时间,对所有SVD方法的未卷积数据和反卷积数据进行评估。此外,我们进行了一项体外研究,来补充我们的电脑调查。我们使用针对患者特定的颈内动脉体模的流量电路设计生成了2DDSA。DSA展示了X射线伪影等因素,噪音,和病人的运动。我们使用不同的SVD变体评估了体外模型的QA参数,并建立了QA参数之间的相关性,注射持续时间,以及未卷积和反卷积数据的速度。
结果:不同的SVD算法变体在流量和反卷积调整的QA参数之间显示出强相关性。此外,我们发现,SVD可以有效地降低QA参数在不同的注射持续时间的变化,增强QA分析参数在神经血管疾病诊断和治疗中的潜力。
结论:在QA分析中实施基于SVD的去卷积技术可以通过有效减少注射时间对血流动力学参数的影响来提高神经血管诊断的准确性和可靠性。
BACKGROUND: Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD-based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations.
OBJECTIVE: Building on the identified limitations in QA, the study aims to adapt SVD-based deconvolution techniques from CTP to QA for IAs. This adaptation seeks to capitalize on the high temporal resolution of QA, despite its two-dimensional nature, to enhance the consistency and accuracy of hemodynamic parameter assessment. The goal is to develop a method that can reliably assess hemodynamic conditions in IAs, independent of injection variables, for improved neurovascular diagnostics.
METHODS: The study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using computational fluid dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time-density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block-circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Next, correlations between QA parameters, injection duration, and inlet velocity were assessed for unconvolved and deconvolved data for all SVD methods. Additionally, we performed an in vitro study, to complement our in silico investigation. We generated a 2D DSA using a flow circuit design for a patient-specific internal carotid artery phantom. The DSA showcases factors like x-ray artifacts, noise, and patient motion. We evaluated QA parameters for the in vitro phantoms using different SVD variants and established correlations between QA parameters, injection duration, and velocity for unconvolved and deconvolved data.
RESULTS: The different SVD algorithm variants showed strong correlations between flow and deconvolution-adjusted QA parameters. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment.
CONCLUSIONS: Implementing SVD-based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters.