Low Wall Shear Stress and High Intra-aneurysmal Pressure are Associated with Ruptured Status of Vertebral Artery Dissecting Aneurysms

Application of Computational Fluid Dynamic Simulation of Parent Blood Flow in the Embolization of Unruptured A1 Aneurysms

OBJECTIVE

To investigate the effect of microcatheter shaping based on the parent artery mainstream line of blood flow simulated using the computational fluid dynamics (CFD) technique on embolization of unruptured aneurysms on the posterior wall of the anterior cerebral artery (ACA) A1 segment.

METHODS

Patients with unruptured cerebral aneurysms on the posterior wall of the ACA A1 segment were retrospectively enrolled and treated with endovascular embolization after microcatheter shaping. The clinical, embolization, and follow-up data were analyzed.

RESULTS

Eight patients were enrolled and treated with endovascular embolization. 8 microcatheters were steam-shaped in vitro and were all successfully navigated to the right location in the in vitro experiment. During the embolization procedure, 7 microcatheters were successfully navigated to the right location for embolization. In the remaining 1 patient who had tortuous cerebral arteries, reshaping of the microcatheter based on the parent artery mainstream of blood flow made successful navigation of the microcatheter to the right place. Complete occlusion was obtained in 7 (87.5%) aneurysms and residual aneurysm neck in the remaining 1 (12.5%). At angiographic follow-up in 6 (75%) patients, the Raymond grade was I in 5 (83.3%) and II in the rest 1 (16.7%).

CONCLUSIONS

Use of computational fluid dynamics simulation of parent artery blood flow for microcatheter shaping in the embolization of unruptured aneurysms on the posterior wall of the ACA A1 segment is safe and effective in navigating the microcatheter to the right location for embolization, resulting in good stability and support for the embolization.

Low shear stress induces macrophage infiltration and aggravates aneurysm wall inflammation via CCL7/CCR1/TAK1/ NF-κB axis

BACKGROUND

This study aimed to elucidate the mechanism by which wall shear stress (WSS) influences vascular walls, accounting for the susceptibility of intracranial aneurysms (IAs) to rupture.

METHOD

We collected blood samples from the sacs of 24 ruptured and 28 unruptured IAs and analyzed the expression of chemokine CCL7 using enzyme-linked immunosorbent assay (ELISA). Univariate and multivariate logistic regression analyses were employed to assess clinical data, aneurysm morphology, and hemodynamics in both groups. Pearson correlation analysis investigated the relationship between CCL7 expression in aneurysm sac blood and WSS. Additionally, we established a bionic cell parallel plate co-culture shear stress model and a mouse low shear stress (LSS) model. The model was modulated using CCL7 recombinant protein, CCR1 inhibitor, and TAK1 inhibitor. We further evaluated CCL7 expression in endothelial cells and the levels of TAK1, NF-κB, IL-1β, and TNF-α in macrophages. Subsequently, the intergroup differences in expression were calculated.

RESULTS

CCL7 expression was significantly higher in the ruptured group compared to the unruptured group. Hemodynamic analysis indicated that WSS was an independent predictor of the risk of aneurysm rupture. A negative linear correlation was observed between CCL7 expression and WSS. Upon addition of CCL7 recombinant protein, upregulation of CCR1 expression and increased levels of p-TAK1 and p-p65 were observed. Treatment with CCR1 and TAK1 inhibitors reduced inflammatory cytokine expression in macrophages under LSS conditions. Overexpression of TAK1 significantly alleviated the inhibitory effects of CCR1 inhibitors on p-p65 and inflammatory cytokines.

CONCLUSION

LSS prompts endothelial cells to secrete CCL7, which, upon binding to the macrophage surface receptor CCR1, stimulates the release of macrophage inflammatory factors via the TAK1/NF-κB signaling pathway. This process exacerbates aneurysm wall inflammation and increases the risk of aneurysm rupture.

Hemodynamic factors of spontaneous vertebral artery dissecting aneurysms assessed with numerical and deep learning algorithms: Role of blood pressure and asymmetry

BACKGROUND AND OBJECTIVES

The pathophysiology of spontaneous vertebral artery dissecting aneurysms (SVADA) is poorly understood. Our goal is to investigate the hemodynamic factors contributing to their formation using computational fluid dynamics (CFD) and deep learning algorithms.

METHODS

We have developed software that can use patient imagery as input to recreate the vertebrobasilar arterial system, both with and without SVADA, which we used in a series of three patients. To obtain the kinematic blood flow data before and after the aneurysm forms, we utilized numerical methods to solve the complex Navier-Stokes partial differential equations. This was accomplished through the application of a finite volume solver (OpenFoam/Helyx OS). Additionally, we trained a neural ordinary differential equation (NODE) to learn and replicate the dynamical streamlines obtained from the computational fluid dynamics (CFD) simulations.

RESULTS

In all three cases, we observed that the equilibrium of blood pressure distributions across the VAs, at a specific vertical level, accurately predicted the future SVADA location. In the two cases where there was a dominant VA, the dissection occurred on the dominant artery where blood pressure was lower compared to the contralateral side. The SVADA sac was characterized by reduced wall shear stress (WSS) and decreased velocity magnitude related to increased turbulence. The presence of a high WSS gradient at the boundary of the SVADA may explain its extension. Streamlines generated by CFD were learned with a neural ordinary differential equation (NODE) capable of capturing the system's dynamics to output meaningful predictions of the flow vector field upon aneurysm formation.

CONCLUSION

In our series, asymmetry in the vertebrobasilar blood pressure distributions at and proximal to the site of the future SVADA accurately predicted its location in all patients. Deep learning algorithms can be trained to model blood flow patterns within biological systems, offering an alternative to the computationally intensive CFD. This technology has the potential to find practical applications in clinical settings.

Development and Evaluation of a Multifrequency Ultrafast Doppler Spectral Analysis (MFUDSA) Algorithm for Wall Shear Stress Measurement: A Simulation and In Vitro Study

Cardiovascular pathology is the leading cause of death and disability in the Western world, and current diagnostic testing usually evaluates the anatomy of the vessel to determine if the vessel contains blockages and plaques. However, there is a growing school of thought that other measures, such as wall shear stress, provide more useful information for earlier diagnosis and prediction of atherosclerotic related disease compared to pulsed-wave Doppler ultrasound, magnetic resonance angiography, or computed tomography angiography. A novel algorithm for quantifying wall shear stress (WSS) in atherosclerotic plaque using diagnostic ultrasound imaging, called Multifrequency ultrafast Doppler spectral analysis (MFUDSA), is presented. The development of this algorithm is presented, in addition to its optimisation using simulation studies and in-vitro experiments with flow phantoms approximating the early stages of cardiovascular disease. The presented algorithm is compared with commonly used WSS assessment methods, such as standard PW Doppler, Ultrafast Doppler, and Parabolic Doppler, as well as plane-wave Doppler. Compared to an equivalent processing architecture with one-dimensional Fourier analysis, the MFUDSA algorithm provided an increase in signal-to-noise ratio (SNR) by a factor of 4-8 and an increase in velocity resolution by a factor of 1.10-1.35. The results indicated that MFUDSA outperformed the others, with significant differences detected between the typical WSS values of moderate disease progression (p = 0.003) and severe disease progression (p = 0.001). The algorithm demonstrated an improved performance for the assessment of WSS and has potential to provide an earlier diagnosis of cardiovascular disease than current techniques allow.