Exploring Injection Bias Reduction Techniques in Quantitative Angiography Using Patient-Specific Phantoms of Intracranial Aneurysm with Diverse Morphologies and Locations

Purpose: In intracranial aneurysm (IA) treatment, digital subtraction angiography (DSA) monitors device-induced hemodynamic changes. Quantitative angiography (QA) provides more precise assessments but is limited by hand-injection variability. This study evaluates correction methods using in vitro phantoms that mimic diverse aneurysm morphologies and locations, addressing the 2D and temporal limitations of DSA. Materials and Methods: We used patient-specific phantoms to replicate three distinct IA morphologies at various Circle of Willis points: the middle cerebral artery (MCA), anterior communicating artery (ACA), and the internal carotid artery (ICA), each varying in size and shape. The diameters of the IA at MCA, ACA and ICA are 10.1, 10 and 7 millimeters, respectively. QA parameters for both non-stenosed and stenosed conditions were measured with 5ml and 10ml contrast media boluses over various injection durations to generate time density curves (TDCs). To address the variability in injection, several singular value decomposition (SVD) variants—standard SVD (sSVD) with Tikhonov regularization, block-circulant SVD (bSVD), and oscillation index SVD (oSVD)—were applied. These methods enabled the extraction of IA impulse response function (IRF), peak height (PHIRF), area under the curve (AUCIRF), and mean transit time (MTT). We evaluated the robustness of bias-reducing methods by observing the variance of these parameters with respect to the injection conditions, and the location and size of the aneurysm. Results: The application of SVD variants—sSVD, bSVD, and oSVD—reduced QA parameter variability due to injection techniques. MTT plots demonstrated a consistent decrease in variability across several injection durations. This reduction in bias and enhanced parameter invariance was evident across different aneurysm sizes and locations, indicating the robustness of SVD methods in standardizing neurovascular diagnostic measures. Conclusions: SVD-based deconvolution improves neurovascular diagnostic accuracy by reducing bias and maintaining consistent results despite varying aneurysm sizes and locations.