Effect of Singular Value Decomposition Algorithms on Removing Injection Variability in 2D Quantitative Angiography of Intracranial Aneurysms

TL;DR

This study evaluates how different SVD algorithms can reduce variability caused by injection durations in 2D angiography of intracranial aneurysms, improving diagnostic accuracy.

Contribution

It introduces the application of various SVD-based deconvolution methods to 2D QA, demonstrating their effectiveness in mitigating injection variability effects.

Findings

SVD reduces variability in QA parameters across injection durations

SVD improves the reliability of flow-related measurements in virtual angiograms

Different SVD variants show varying effectiveness in deconvolution

Abstract

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. 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. 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. 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.