Correction: Bisighini et al. Fabrication of Compliant and Transparent Hollow Cerebral Vascular Phantoms for In Vitro Studies Using 3D Printing and Spin-Dip Coating. Materials 2023, 16, 166.

In the original publication [...].

Machine learning and reduced order modelling for the simulation of braided stent deployment.

Endoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel is not easily predictable and medical volumetric images alone may be insufficient to plan the treatment satisfactorily. Therefore, here we propose a fast and accurate machine learning and reduced order modelling framework, based on finite element simulations, to assist practitioners in the planning and interventional stages. It consists of a first classification step to determine a priori whether a simulation will be successful (good conformity between stent and vessel) or not from a clinical perspective, followed by a regression step that provides an approximated solution of the deployed stent configuration. The latter is achieved using a non-intrusive reduced order modelling scheme that combines the proper orthogonal decomposition algorithm and Gaussian process regression. The workflow was validated on an idealized intracranial artery with a saccular aneurysm and the effect of six geometrical and surgical parameters on the outcome of stent deployment was studied. We trained six machine learning models on a dataset of varying size and obtained classifiers with up to 95% accuracy in predicting the deployment outcome. The support vector machine model outperformed the others when considering a small dataset of 50 training cases, with an accuracy of 93% and a specificity of 97%. On the other hand, real-time predictions of the stent deployed configuration were achieved with an average validation error between predicted and high-fidelity results never greater than the spatial resolution of 3D rotational angiography, the imaging technique with the best spatial resolution (0.15 mm). Such accurate predictions can be reached even with a small database of 47 simulations: by increasing the training simulations to 147, the average prediction error is reduced to 0.07 mm. These results are promising as they demonstrate the ability of these techniques to achieve simulations within a few milliseconds while retaining the mechanical realism and predictability of the stent deployed configuration.

Fabrication of Compliant and Transparent Hollow Cerebral Vascular Phantoms for In Vitro Studies Using 3D Printing and Spin-Dip Coating.

Endovascular surgery through flow diverters and coils is increasingly used for the minimally invasive treatment of intracranial aneurysms. To study the effectiveness of these devices, in vitro tests are performed in which synthetic vascular phantoms are typically used to reproduce in vivo conditions. In this paper, we propose a manufacturing process to obtain compliant and transparent hollow vessel replicas to assess the mechanical behaviour of endovascular devices and perform flow measurements. The vessel models were obtained in three main steps. First, a mould was 3D-printed in a water-soluble material; two techniques, fusion deposition modelling and stereolithography, were compared for this purpose. Then, the mould was covered with a thin layer of silicone through spin-dip coating, and finally, when the silicone layer solidified, it was dissolved in a hot water bath. The final models were tested in terms of the quality of the final results, the mechanical properties of the silicone, thickness uniformity, and transparency properties. The proposed approach makes it possible to produce models of different sizes and complexity whose transparency and mechanical properties are suitable for in vitro experiments. Its applicability is demonstrated through idealised and patient-specific cases.