Hemodynamic Investigation of the Effectiveness of a Two Overlapping Flow Diverter Configuration for Cerebral Aneurysm Treatment

Repeat Flow Diversion for Retreatment of Incompletely Occluded Large Complex Symptomatic Cerebral Aneurysms: A Retrospective Case Series

BACKGROUND AND OBJECTIVES

Data regarding radiographic occlusion rates after repeat flow diversion after initial placement of a flow diverter (FD) in large intracranial aneurysms are limited. We report clinical and angiographic outcomes on 7 patients who required retreatment with overlapping FDs after initial flow diversion for large intracranial aneurysms.

METHODS

We performed a retrospective review of a prospectively maintained database of cerebrovascular procedures performed at our institution from 2017 to 2021. We identified patients who underwent retreatment with overlapping FDs for large (>10 mm) cerebral aneurysms after initial flow diversion. At last angiographic follow-up, occlusion grade was evaluated using the O'Kelly-Marotta (OKM) grading scale.

RESULTS

Seven patients (median age 57 years) with cerebral aneurysms requiring retreatment were identified. The most common aneurysm location was the ophthalmic internal carotid artery (n = 3) and basilar trunk (n = 3). There were 4 fusiform and 3 saccular aneurysms. The median aneurysm width was 18 mm; the median neck size for saccular aneurysms was 7 mm; and the median dome-to-neck ratio was 2.8. The median time to retreatment was 9 months, usually due to symptomatic mass effect. After retreatment, the median clinical follow-up was 36 months, MRI/magnetic resonance angiography follow-up was 15 months, and digital subtraction angiography follow-up was 14 months. Aneurysm occlusion at last angiographic follow-up was graded as OKM A (total filling, n = 1), B (subtotal filling, n = 2), C (early neck remnant, n = 3), and D (no filling, n = 0). All patients with symptomatic improvement were OKM C, whereas patients with worsened symptom burden were OKM A or B. Two patients required further open surgical management for definitive management of the aneurysm remnant.

CONCLUSION

Although most patients demonstrated a decrease in aneurysm remnant size, many had high-grade persistent filling (OKM grades A or B) in this subset of mostly large fusiform aneurysms. Larger studies with longer follow-up are warranted to optimize treatment strategies for atypical aneurysm remnants after repeat flow diversion.

De novo expansion formation in the outer curvature of the internal carotid artery after flow diverter deployment for an infectious cavernous carotid aneurysm: illustrative case

BACKGROUND

Infectious aneurysms very rarely occur in the cavernous carotid artery. Recently, treatment by flow diverter implantation with preservation of the parent artery has been the treatment of choice.

OBSERVATIONS

A 64-year-old woman presented with stenosis at the C5 segment of the left internal carotid artery (ICA), followed by ocular symptoms within 2 weeks, with a de novo aneurysm in the left cavernous carotid artery and wall irregularity with stenosis from the C2 to C5 segments of the left ICA. Antimicrobial therapy was given for 6 weeks, and a Pipeline Flex Shield was implanted. Angiography 6 months after treatment showed complete obliteration of the infectious aneurysm and improvement of the stenosis. However, de novo expansions were formed in the outer curvature of C3 and C4 segments of the ICA where the Pipeline device had been deployed.

LESSONS

Aneurysms that develop rapidly and show shape changes over time, accompanied by fever and inflammation, may be associated with an infection. Because of the fragility in the irregular wall of the parent vessel associated with infectious aneurysms, de novo expansion may form in the outer curvature of the parent vessel after flow diverter placement; thus, careful follow-up is necessary.

CFD Study of the Effect of the Angle Pattern on Iliac Vein Compression Syndrome

Iliac vein compression syndrome (IVCS, or May-Thurner syndrome) occurs due to the compression of the left common iliac vein between the lumbar spine and right common iliac artery. Because most patients with compression are asymptomatic, the syndrome is difficult to diagnose based on the degree of anatomical compression. In this study, we investigated how the tilt angle of the left common iliac vein affects the flow patterns in the compressed blood vessel using three-dimensional computational fluid dynamic (CFD) simulations to determine the flow fields generated after compression sites. A patient-specific iliac venous CFD model was created to verify the boundary conditions and hemodynamic parameter set in this study. Thirty-one patient-specific CFD models with various iliac venous angles were developed using computed tomography (CT) angiograms. The angles between the right or left common iliac vein and inferior vena cava at the confluence level of the common iliac vein were defined as α1 and α2. Flow fields and vortex locations after compression were calculated and compared according to the tilt angle of the veins. Our results showed that α2 affected the incidence of flow field disturbance. At α2 angles greater than 60 degrees, the incidence rate of blood flow disturbance was 90%. In addition, when α2 and α1 + α2 angles were used as indicators, significant differences in tilt angle were found between veins with laminar, transitional, and turbulent flow (p < 0.05). Using this mathematical simulation, we concluded that the tilt angle of the left common iliac vein can be used as an auxiliary indicator to determine IVCS and its severity, and as a reference for clinical decision making.

Evaluation of hemodynamic effects of different inferior vena cava filter heads using computational fluid dynamics

Inferior vena cava (IVC) filters are used to prevent pulmonary embolism in patients with deep vein thrombosis for whom anticoagulation is unresponsive. The head is a necessary structure for an Inferior vena cava filter (IVCF) in clinic use. At present, there are various head configurations for IVCFs. However, the effect of head pattern on the hemodynamics of IVCF is still a matter of unclear. In this study, computational fluid dynamics is used to simulate non-Newtonian blood flows around four IVCFs with different heads inside an IVC model, in which the Denali filter with a solid and hooked head is employed as a prototype, and three virtual variants are reconstructed either with a no-hook head or with a through-hole head for comparison. The simulation results show that the through-hole head can effectively avoid the recirculation region and weaken the blood flow stasis closely downstream the IVCF head. The shape change of the filter head has no significant effect on the blood flow acceleration inside the IVCF cone as well as little influence on the wall shear stress (WSS) distribution on the filter wire surface and IVC wall. The structure pattern of filter head greatly affects the flow resistance of its own. However, the flow drag of filter head only occupies a small proportion of the total resistance of IVCF. Therefore, to reduce the flow resistance of an IVCF should optimize its whole structure.

Deep Learning for Computational Hemodynamics: A Brief Review of Recent Advances

Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better understanding various medical conditions, designing more effective drug delivery systems, and developing novel diagnostic methods and treatments. However, despite significant advances in computational technology and resources, the expensive computational cost of these simulations still hinders their transformation from a research interest to a clinical tool. This bottleneck is even more severe for image-based, patient-specific CFD simulations with realistic boundary conditions and complex computational domains, which make such simulations excessively expensive. To address this issue, deep learning approaches have been recently explored to accelerate computational hemodynamics simulations. In this study, we review recent efforts to integrate deep learning with CFD and discuss the applications of this approach in solving hemodynamics problems, such as blood flow behavior in aorta and cerebral arteries. We also discuss potential future directions in the field. In this review, we suggest that incorporating physiologic understandings and underlying fluid mechanics laws in deep learning models will soon lead to a paradigm shift in the development novel non-invasive computational medical decisions.