A Computational Framework for Pre-Interventional Planning of Peripheral Arteriovenous Malformations

Abstract

Abstract Purpose Peripheral arteriovenous malformations (pAVMs) are congenital lesions characterised by abnormal high-flow, low-resistance vascular connections—the so-called nidus—between arteries and veins. The mainstay treatment typically involves the embolisation of the nidus, however the complexity of pAVMs often leads to uncertain outcomes. This study aims at developing a simple, yet effective computational framework to aid the clinical decision making around the treatment of pAVMs using routinely acquired clinical data. Methods A computational model was developed to simulate the pre-, intra-, and post-intervention haemodynamics of a patient-specific pAVM. A porous medium of varying permeability was employed to simulate the sclerosant effect on the nidus haemodynamics. Results were compared against clinical data (digital subtraction angiography, DSA, images) and experimental flow-visualization results in a 3D-printed phantom of the same pAVM. Results The computational model allowed the simulation of the pAVM haemodynamics and the sclerotherapy-induced changes at different interventional stages. The predicted inlet flow rates closely matched the DSA-derived data, although the post-intervention one was overestimated, probably due to vascular system adaptations not accounted for numerically. The nidus embolization was successfully captured by varying the nidus permeability and increasing its hydraulic resistance from 0.330 to 3970 mmHg s ml−1. The nidus flow rate decreased from 71% of the inlet flow rate pre-intervention to 1%: the flow completely bypassed the nidus post-intervention confirming the success of the procedure. Conclusion The study demonstrates that the haemodynamic effects of the embolisation procedure can be simulated from routinely acquired clinical data via a porous medium with varying permeability as evidenced by the good qualitative agreement between numerical predictions and both in vivo and in vitro data. It provides a fundamental building block towards a computational treatment-planning framework for AVM embolisation.