Angiographic velocimetry analysis using contrast dilution gradient method with a 1000 frames per second photon-counting detector.

Purpose: Contrast dilution gradient (CDG) analysis is a quantitative method allowing blood velocity estimation using angiographic acquisitions. Currently, CDG is restricted to peripheral vasculature due to the suboptimal temporal resolution of current imaging systems. We investigate extension of CDG methods to the flow conditions of proximal vasculature using 1000 frames per second (fps) high-speed angiographic (HSA) imaging. Approach: We performed in-vitro HSA acquisitions using the XC-Actaeon detector and 3D-printed patient-specific phantoms. The CDG approach was used for blood velocity estimation expressed as the ratio of temporal and spatial contrast gradients. The gradients were extracted from 2D contrast intensity maps synthesized by plotting intensity profiles along the arterial centerline at each frame. In-vitro results obtained at various frame rates via temporal binning of 1000 fps data were retrospectively compared to computational fluid dynamics (CFD) velocimetry. Full-vessel velocity distributions were estimated at 1000 fps via parallel line expansion of the arterial centerline analysis. Results: Using HSA, the CDG method displayed agreement with CFD at or above 250 fps [mean-absolute error (MAE): 2.6±6.3 cm/s, p=0.05]. Relative velocity distributions correlated well with CFD at 1000 fps with universal underapproximation due to effects of pulsatile contrast injection (MAE: 4.3 cm/s). Conclusions: Using 1000 fps HSA, CDG-based extraction of velocities across large arteries is possible. The method is sensitive to noise; however, image processing techniques and a contrast injection, which adequately fills the vessel assist algorithm accuracy. The CDG method provides high resolution quantitative information for rapidly transient flow patterns observed in arterial circulation.

Use of high-speed angiography HSA-derived boundary conditions and Physics Informed Neural Networks (PINNs) for comprehensive estimation of neurovascular hemodynamics.

Purpose: Physics-informed neural networks (PINNs) and computational fluid dynamics (CFD) have both demonstrated an ability to derive accurate hemodynamics if boundary conditions (BCs) are known. Unfortunately, patient-specific BCs are often unknown, and assumptions based upon previous investigations are used instead. High speed angiography (HSA) may allow extraction of these BCs due to the high temporal fidelity of the modality. We propose to investigate whether PINNs using convection and Navier-Stokes equations with BCs derived from HSA data may allow for extraction of accurate hemodynamics in the vasculature. Materials and Methods: Imaging data generated from in vitro 1000 fps HSA, as well as simulated 1000 fps angiograms generated using CFD were utilized for this study. Calculations were performed on a 3D lattice comprised of 2D projections temporally stacked over the angiographic sequence. A PINN based on an objective function comprised of the Navier-Stokes equation, the convection equation, and angiography-based BCs was used for estimation of velocity, pressure and contrast flow at every point in the lattice. Results: Imaging-based PINNs show an ability to capture such hemodynamic phenomena as vortices in aneurysms and regions of rapid transience, such as outlet vessel blood flow within a carotid artery bifurcation phantom. These networks work best with small solution spaces and high temporal resolution of the input angiographic data, meaning HSA image sequences represent an ideal medium for such solution spaces. Conclusions: The study shows the feasibility of obtaining patient-specific velocity and pressure fields using an assumption-free data driven approach based purely on governing physical equations and imaging data.

Geometrically independent contrast dilution gradient (CDG) velocimetry using photon-counting 1000 fps High Speed Angiography (HSA) for 2D velocity distribution estimation.

Purpose: Previous studies have demonstrated the efficacy of contrast dilution gradient (CDG) analysis in determining large vessel velocity distributions from 1000 fps high-speed angiography (HSA). However, the method required vessel centerline extraction, which made it applicable only to non-tortuous geometries using a highly specific contrast injection technique. This study seeks to remove the need for a priori knowledge regarding the direction of flow and modify the vessel sampling method to make the algorithm more robust to non-linear geometries. Materials and Methods: 1000 fps HSA acquisitions were obtained in vitro with a benchtop flow loop using the XC-Actaeon (Varex Inc.) photon-counting detector, and in silico using a passive-scalar transport model within a computational fluid dynamics (CFD) simulation. CDG analyses were obtained using gridline sampling across the vessel, and subsequent 1D velocity measurement in both the x- and y-directions. The velocity magnitudes derived from the component CDG velocity vectors were aligned with CFD results via co-registration of the resulting velocity maps and compared using mean absolute percent error (MAPE) between pixels values in each method after temporal averaging of the 1-ms velocity distributions. Results: Regions well-saturated with contrast throughout the acquisition showed agreement when compared to CFD (MAPE of 18% for the carotid bifurcation inlet and MAPE of 27% for the internal carotid aneurysm), with respective completion times of 137 seconds and 5.8 seconds. Conclusions: CDG may be used to obtain velocity distributions in and surrounding vascular pathologies provided the contrast injection is sufficient to provide a gradient, and diffusion of contrast through the system is negligible.

Multi-angled simultaneous biplane High-Speed Angiography (HSA) of patient-specific 3D-printed aneurysm phantoms using 1000 fps CdTe Photon-Counting Detectors (PCD's).

1000 fps HSA enables visualization of flow details, which may be important in accurately guiding interventional procedures; however, single-plane imaging may lack clear visualization of vessel geometry and flow detail. The previously presented high-speed orthogonal biplane imaging may overcome these limitations but may still result in foreshortening of vessel morphology. In certain morphologies, acquiring two non-orthogonal biplane projections at multiple angles can provide better flow detail rather than a standard orthogonal biplane acquisition. Flow studies of aneurysm models were performed, where simultaneous biplane acquisitions at various angles separating the two detector views allowed for better evaluation of morphology and flow. 3D-printed, patient-specific internal carotid artery aneurysm models were imaged with various non-orthogonal angles between the two high-speed photon-counting detectors (7.5 cm x 5 cm FOV) to provide frame-correlated simultaneous 1000-fps image sequences. Fluid dynamics were visualized in multi-angled planes of each model using automated injections of iodine contrast media. The resulting dual simultaneous frame-correlated 1000-fps acquisitions from multiple planes of each aneurysm model provided improved visualization of complex aneurysm geometries and flow streamlines. Multi-angled biplane acquisitions with frame correlation allows for further understanding of aneurysm morphology and flow details: additionally, the ability to recover fluid dynamics at depth enables accurate analysis of 3D flow streamlines, and it is expected that multiple-planar views will enable better volumetric flow visualization and quantification. Such better visualization has the potential to improve interventional procedures.

Comparison of pulsatile flow dynamics before and after endovascular intervention in 3D-printed patient-specific internal carotid artery aneurysm models using 1000 fps photon-counting detectors for Simultaneous Biplane High Speed Angiography (SB-HSA).

A significant challenge regarding the treatment of aneurysms is the variability in morphology and analysis of abnormal flow. With conventional DSA, low frame rates limit the flow information available to clinicians at the time of the vascular intervention. With 1000 fps High-Speed Angiography (HSA), high frame rates enable flow details to be better resolved for endovascular interventional guidance. The purpose of this work is to demonstrate how 1000 fps biplane-HSA can be used to differentiate flow features, such as vortex formation and endoleaks, amongst patient-specific internal carotid artery aneurysm phantoms pre- and post-endovascular intervention using an in-vitro flow setup. The aneurysm phantoms were attached to a flow loop configured to a carotid waveform, with automated injections of contrast media. Simultaneous Biplane High-Speed Angiographic (SB- HSA) acquisitions were obtained at 1000 fps using two photon-counting detectors with the respective aneurysm and inflow/ outflow vasculature in the FOV. After x-rays were turned on, the detector acquisitions occurred simultaneously, during which iodine contrast was injected at a continuous rate. A pipeline stent was then deployed to divert flow from the aneurysm, and image sequences were once again acquired using the same parameters. Optical Flow, an algorithm that calculates velocity based on spatial-temporal intensity changes between pixels, was used to derive velocity distributions from HSA image sequences. Both the image sequences and velocity distributions indicate detailed changes in flow features amongst the aneurysms before and after deployment of the interventional device. SB-HSA can provide detailed flow analysis, including streamline and velocity changes, which may be beneficial for interventional guidance.

2D vessel contrast dilution gradient (CDG) analysis using 1000 fps high speed angiography (HSA) for velocity distribution estimation.

Purpose: Contrast dilution gradient (CDG) analysis is a technique used to extract velocimetric 2D information from digitally subtracted angiographic (DSA) acquisitions. This information may then be used by clinicians to quantitatively assess the effects of endovascular treatment on flow conditions surrounding pathologies of interest. The method assumes negligible diffusion conditions, making 1000 fps high speed angiography (HSA), in which diffusion between 1 ms frames may be neglected, a strong candidate for velocimetric analysis using CDG. Previous studies have demonstrated the success of CDG analysis in obtaining velocimetric one-dimensional data at the arterial centerline of simple vasculature. This study seeks to resolve velocity distributions across the entire vessel using 2D-CDG analysis with HSA acquisitions. Materials and Methods: HSA acquisitions for this study were obtained in vitro with a benchtop flow loop at 1000 fps using the XC-Actaeon (Direct Conversion Inc.) photon counting detector. 2D-CDG analyses were compared with computational fluid dynamics (CFD) via automatic co-registration of the results from each velocimetry method. This comparison was performed using mean absolute error between pixel values in each method (after temporal averaging). Results: CDG velocity magnitudes were slightly under approximated relative to CFD results (mean velocity: 27 cm/s, mean absolute error: 4.3 cm/s) as a result of incomplete contrast filling. Relative 2D spatial velocity distributions in CDG analysis agreed well with CFD distributions qualitatively. Conclusions: CDG may be used to obtain velocity distributions in and surrounding vascular pathologies provided diffusion is negligible relative to convection in the flow, given a continuous gradient of contrast.

Semi-automatic Co-Registration of 3D CFD Vascular Geometry to 1000 FPS High-Speed Angiographic (HSA) Projection Images for Flow Determination Comparisons.

Image co-registration is an important tool that is commonly used to quantitatively or qualitatively compare information from images or data sets that vary in time, origin, etc. This research proposes a method for the semi-automatic co-registration of the 3D vascular geometry of an intracranial aneurysm to novel high-speed angiographic (HSA) 1000 fps projection images. Using the software Tecplot 360, 3D velocimetry data generated from computational fluid dynamics (CFD) for patient-specific vasculature models can be extracted and uploaded into Python. Dilation, translation, and angular rotation of the 3D velocimetry data can then be performed in order to co-register its geometry to corresponding 2D HSA projection images of the 3D printed vascular model. Once the 3D CFD velocimetry data is geometrically aligned, a 2D velocimetry plot can be generated and the Sørensen-Dice coefficient can be calculated in order to determine the success of the co-registration process. The co-registration process was performed ten times for two different vascular models and had an average Sørensen-Dice coefficient of 0.84 ± 0.02. The method presented in this research allows for a direct comparison between 3D CFD velocimetry data and in-vitro 2D velocimetry methods. From the 3D CFD, we can compare various flow characteristics in addition to velocimetry data with HSA-derived flow metrics. The method is robust to other vascular geometries as well.