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Subarachnoid hemorrhage (SAH), typically due to intracranial aneurysms, demands precise imaging for effective treatment. Digital Subtraction Angiography (DSA), despite being the gold standard, broadly visualizes cerebral blood flow, potentially masking key details in areas. This study introduces an approach integrating a 3D vascular atlas with 2D DSA images to allow targeted quantitative analysis in these crucial regions, thus enhancing diagnostic accuracy during interventions. Initially, DSA data was examined to ascertain the injection site. Following this, the appropriate viewing angle was determined to align accurately with the 3D vascular atlas. Utilizing this atlas, regions corresponding to the areas indicated as perfused were selected. Concurrently, a mask representing the perfused areas was created from the DSA sequence. This mask facilitated the initial coarse alignment of the projected 3D atlas to the DSA perfused territory deformable registration techniques, ensuring a precise overlay with the DSAs perfused territories. The performance of each overlay was measured using the Structural Similarity Index Measure (SSIM). The coregistration process revealed that deformable registrations was essential to achieve precise overlays of the 3D atlas projections with the 2D DSA perfused areas. This approach enabled the extraction of targeted quantitative angiography parameters, essential for detailed vascular assessment in subarachnoid hemorrhage cases. The integration of 3D atlas registration with 2D DSA projections facilitates a more precise and targeted diagnostic process for SAH during critical interventions. This image processing strategy enhances the visualization of affected arterial territories, potentially improving the accuracy of diagnostics and supporting better informed clinical decisions at the time of intervention, Comment: 5 pages, 2 figures, to be published in SPIE Medical Imaging 2025 conference proceeds

This study evaluates a multimodal machine learning framework for predicting treatment outcomes in intracranial aneurysms (IAs). Combining angiographic parametric imaging (API), patient biomarkers, and disease morphology, the framework aims to enhance prognostic accuracy. Data from 340 patients were analyzed, with separate deep neural networks processing quantitative and categorical data. These networks' pre decision layers were concatenated and inputted into a final predictive network. Various data augmentation strategies, including Synthetic Minority Oversampling Technique for Nominal and Continuous data (SMOTE NC), addressed dataset imbalances. Performance metrics, evaluated through Monte Carlo cross validation, showed significant improvements with augmentation, particularly in intermediate fusion models. This study validates the framework's efficacy in accurately predicting IA treatment outcomes, demonstrating that data augmentation techniques can substantially enhance model performance., Comment: 6 pages

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. We used a patient-specific phantom 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 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 invariance of these parameters with respect to the injection conditions, and the location and size of the aneurysm. The application of SVD variants, sSVD, bSVD, and oSVD, significantly reduced QA parameter variability due to injection techniques., Comment: arXiv admin note: text overlap with arXiv:2411.03655

This study leverages convolutional neural networks to enhance the temporal resolution of 3D angiography in intracranial aneurysms focusing on the reconstruction of volumetric contrast data from sparse and limited projections. Three patient-specific IA geometries were segmented and converted into stereolithography files to facilitate computational fluid dynamics simulations. These simulations first modeled blood flow under steady conditions with varying inlet velocities: 0.25 m/s, 0.35 m/s, and 0.45 m/s. Subsequently, 3D angiograms were simulated by labeling inlet particles to represent contrast bolus injections over durations of 0.5s, 1.0s, 1.5s, and 2.0s. The angiographic simulations were then used within a simulated cone beam C arm CT system to generate in-silico rotational DSAs, capturing projections every 10 ms over a 220-degree arc at 27 frames per second. From these simulations, both fully sampled (108 projections) and truncated projection datasets were generated the latter using a maximum of 49 projections. High fidelity volumetric images were reconstructed using a Parker weighted Feldkamp Davis Kress algorithm. A modified U Net CNN was subsequently trained on these datasets to reconstruct 3D angiographic volumes from the truncated projections. The network incorporated multiple convolutional layers with ReLU activations and Max pooling, complemented by upsampling and concatenation to preserve spatial detail. Model performance was evaluated using mean squared error (MSE). Evaluating our U net model across the test set yielded a MSE of 0.0001, indicating good agreement with ground truth reconstructions and demonstrating acceptable capabilities in capturing relevant transient angiographic features. This study confirms the feasibility of using CNNs for reconstructing 3D angiographic images from truncated projections.

This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro methods. We utilized DSA data from in-silico and in-vitro patient-specific intracranial aneurysm models. To remove projection bias, PLC for various views were developed by co-registering the pre-existing 3D vascular geometry mask with the DSA projections, followed by ray tracing to determine paths across 3D vessel structures. These maps were used to normalize the logarithmic angiographic images, correcting for projection-induced foreshortening across different angles. Subsequently, we focused on eliminating injection bias by analyzing the corrected angiograms under varied projection views, injection rates, and flow conditions. Regions of interest at the aneurysm dome and inlet were placed to extract Time Density Curves for the lesion and the arterial input function, respectively. Using three standard SVD methodologies, we extracted the aneurysm Impulse Response function (IRF) and its associated parameters Peak Height (PHIRF), Area Under the Curve (AUCIRF), and Mean Transit Time (MTT). Our methodology employing PLC and SVD-based deconvolution ensures reliable quantitative angiographic measurements across varying conditions, supporting consistent assessments of disease severity and treatment efficacy. This approach significantly enhances intrapatient and intraprocedural reliability in neurovascular diagnostics., Comment: 11 pages, 6figures

In Angiographic Parametric Imaging (API), accurate estimation of parameters from Time Density Curves (TDC) is crucial. However, these estimations are often marred by errors arising from factors such as patient motion, procedural preferences, image noise, and injection variability. While fitting methods like gamma-variate fitting offer a solution to recover incomplete or corrupted TDC data, they might also introduce unforeseen biases. This study investigates the trade-offs and benefits of employing gamma-variate fitting on virtual angiograms to enhance the precision of API biomarkers. Utilizing Computational Fluid Dynamics (CFD) in patient specific 3D geometries, we generated a series of high-definition virtual angiograms at distinct inlet velocities: 0.25m/s, 0.35m/s, and 0.45m/s. These velocities were investigated across injection durations ranging from 0.5s to 2.0s. From these angiograms, TDCs for aneurysms and their corresponding inlets were constructed. To emulate typical clinical challenges, we introduced noise, simulated patient motion, and generated temporally incomplete data sets. These modified TDCs underwent gamma-variate fitting. We quantified both the original and fitted TDC curves using standard angiography metrics such as Cross-Correlation (Cor), Time to Peak (TTP), Mean Transit Time (MTT), Peak Height (PH), Area Under the Curve (AUC), and Maximum Gradient (Max-Gr) for a comprehensive comparison. TDCs enhanced by gamma-variate fitting exhibited a robust correlation with vascular flow dynamics. Our results affirm that gamma-variate fitting can adeptly restore TDCs from fragmentary sequences, elevating the precision of derived API parameters.

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.