Stenting as porous media in anatomically accurate geometries. A comparison of models and spatial heterogeneity

Computer Aided Intracranial Aneurysm Treatment Based on 2D/3D Mapping, Virtual Deployment and Online Distal Marker Detection

PURPOSE

To introduce a computational tool for peri-interventional intracranial aneurysm treatment guidance that maps preoperative planning information from simulation onto real-time X-Ray imaging.

METHODS

Preoperatively, multiple flow diverter (FD) devices are simulated based on the 3D mesh of the vessel to treat, to choose the optimal size and location. In the peri-operative stage, this 3D information is aligned and mapped to the continuous 2D-X-Ray scan feed from the operating room. The current flow diverter position in the 3D model is estimated by automatically detecting the distal FD marker locations and mapping them to the treated vessel. This allows to visually assess the possible outcome of releasing the device at the current position, and compare it with the one chosen pre-operatively.

RESULTS

The full pipeline was validated using retrospectively collected biplane images from four different patients (5 3D-DSA datasets in total). The distal FD marker detector obtained an average F1-score of 0.67 ( ± 0.224 ) in 412 2D-X-Ray scans. After aligning 3D-DSA + 2D-X-Ray datasets, the average difference between simulated and deployed positions was 0.832 mm ( ± 0.521 mm). Finally, we qualitatively show that the proposed approach is able to display the current location of the FD compared to their pre-operatively planned position.

CONCLUSIONS

The proposed method allows to support the FD deployment procedure by merging and presenting preoperative simulation information to the interventionists, aiding them to make more accurate and less risky decisions.

Design space exploration of flow diverter hydraulic resistance parameters in sidewall intracranial aneurysms

Intracranial aneurysms are nowadays treated with endovascular flow diverter devices to avoid sac rupture. This study explores how different linear and quadratic hydrodynamic resistance parameters reduce the flow in the sac for five patient-specific sidewall aneurysms.The 125 performed blood flow simulations included the stents using a Darcy-Forcheimer porous layer approach based on real-life stent characteristics. Time- and space-averaged velocity magnitudes were strongly affected by the linear coefficient with a power-law relationship. Quadratic coefficients alter the flow in a minor way due to the low-velocity levels in the aneurysm sac and neck region.

Assessing a heterogeneous model for accounting for endovascular devices in hemodynamic simulations of cerebral aneurysms

The heterogeneous model developed by Berod et al [Int J Numer Method Biomed Eng 38, 2021] for representing the hemodynamic effects of endovascular prostheses is applied to a series of 10 patient specific cerebral aneurysms, 6 being treated by flow diverters, 4 being equipped with WEBs. Two markers correlated with the medical outcome of the treatment are used to assess the potential of the model, namely the saccular mean velocity and the inflow rate at the neck of the aneurysm. The comparison with the corresponding wire-resolved simulations is very favorable in both cases, and the model-based simulations also retrieve the jetting-type flows generated downstream of the struts. Noteworthy, the very same model was used for representing the flow diverters and the WEBs, showing the versatility and robustness of the heterogeneous modeling of the devices.

Cerebral aneurysm flow diverter modeled as a thin inhomogeneous porous medium in hemodynamic simulations

Rapid and accurate simulation of cerebral aneurysm flow modifications by flow diverters (FDs) can help improving patient-specific intervention and predicting treatment outcome. However, when FD devices are explicitly represented in computational fluid dynamics (CFD) simulations, flow around the stent wires must be resolved, leading to high computational cost. Classic porous medium (PM) methods can reduce computational expense but cannot capture the inhomogeneous FD wire distribution once implanted on a cerebral artery and thus cannot accurately model the post-stenting aneurysmal flow. We report a novel approach that models the FD flow modification as a thin inhomogeneous porous medium (iPM). It improves over the classic PM approaches in two ways. First, the FD is more appropriately treated as a thin screen, which is more accurate than the classic 3D-PM-based Darcy-Forchheimer relation. Second, pressure drop is calculated cell-by-cell using the local FD geometric parameters across an inhomogeneous PM. We applied the iPM technique to simulating the post-stenting hemodynamics of three patient-specific aneurysms. To test its accuracy and speed, we compared the results from the iPM technique against CFD simulations with explicit FD devices. The iPM CFD ran 500% faster than the explicit CFD while achieving 94%-99% accuracy; thus, iPM is a promising clinical bedside modeling tool to assist endovascular interventions with FD and stents.

Prediction of Cerebral Aneurysm Hemodynamics With Porous-Medium Models of Flow-Diverting Stents via Deep Learning

The interventional treatment of cerebral aneurysm requires hemodynamics to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in calculating cerebral aneurysm hemodynamics before and after flow-diverting (FD) stent placement. However, the complex operation (such as the construction and placement simulation of fully resolved or porous-medium FD stent) and high computational cost of CFD hinder its application. To solve these problems, we applied aneurysm hemodynamics point cloud data sets and a deep learning network with double input and sampling channels. The flexible point cloud format can represent the geometry and flow distribution of different aneurysms before and after FD stent (represented by porous medium layer) placement with high resolution. The proposed network can directly analyze the relationship between aneurysm geometry and internal hemodynamics, to further realize the flow field prediction and avoid the complex operation of CFD. Statistical analysis shows that the prediction results of hemodynamics by our deep learning method are consistent with the CFD method (error function <13%), but the calculation time is significantly reduced 1,800 times. This study develops a novel deep learning method that can accurately predict the hemodynamics of different cerebral aneurysms before and after FD stent placement with low computational cost and simple operation processes.

Intra-saccular device modeling for treatment planning of intracranial aneurysms: from morphology to hemodynamics

MOTIVATION

Intra-saccular devices (ID), developed for the treatment of bifurcation aneurysms, offer new alternatives for treating complex terminal and bifurcation aneurysms. In this work, a complete workflow going from medical images to post-treatment CFD analysis is described and used in the assessment of a concrete clinical problem.

MATERIALS AND METHODS

Two different intra-saccular device sizes were virtually implanted in 3D models of the patient vasculature using the ID-Fit method. After deployment, the local porosity at the closed end of the device in contact with the blood flow was computed. This porosity was then used to produce a CFD porous medium model of the device. Velocities and wall shear stress were assessed for each model.

RESULTS

Six patients treated with intra-saccular devices were included in this work. For each case, 2 different device sizes were virtually implanted and 3 CFD simulations were performed: after deployment simulation with each size and before deployment simulation (untreated). A visible reduction in velocities was observed after device implantation. Velocity and WSS reduction was statistically significant (K-S statistics, [Formula: see text]).

CONCLUSIONS

Placement of different device size can lead to a partial filling of the aneurysm, either at the dome or at the neck, depending on the particular positioning by the interventionist. The methodology used in this work can have a strong clinical impact, since it provides additional information in the process of device selection using preoperative data.