Rapid Virtual Stenting for Intracranial Aneurysms

A CT-based predictive model for stent-induced vessel damage: application to type B aortic dissection

OBJECTIVES

The distal stent-induced new entry (distal SINE) is a life-threatening device-related complication after thoracic endovascular aortic repair (TEVAR). However, risk factors for distal SINE are not fully determined, and prediction models are lacking. This study aimed to establish a predictive model for distal SINE based on the preoperative dataset.

METHODS

Two hundred and six patients with Stanford type B aortic dissection (TBAD) that experienced TEVAR were involved in this study. Among them, thirty patients developed distal SINE. Pre-TEVAR morphological parameters were measured based on the CT-reconstructed configurations. Virtual post-TEVAR morphological and mechanical parameters were computed via the virtual stenting algorithm (VSA). Two predictive models (PM-1 and PM-2) were developed and presented as nomograms to help risk evaluation of distal SINE. The performance of the proposed predictive models was evaluated and internal validation was conducted.

RESULTS

Machine-selected variables for PM-1 included key pre-TEVAR parameters, and those for PM-2 included key virtual post-TEVAR parameters. Both models showed good calibration in both development and validation subsamples, while PM-2 outperformed PM-1. The discrimination of PM-2 was better than PM-1 in the development subsample, with an optimism-corrected area under the curve (AUC) of 0.95 and 0.77, respectively. Application of PM-2 in the validation subsample presented good discrimination with an AUC of 0.9727. The decision curve demonstrated that PM-2 was clinically useful.

CONCLUSION

This study proposed a predictive model for distal SINE incorporating the CT-based VSA. This predictive model could efficiently predict the risk of distal SINE and thus might contribute to personalized intervention planning.

CLINICAL RELEVANCE STATEMENT

This study established a predictive model to evaluate the risk of distal SINE based on the pre-stenting CT dataset and planned device information. With an accurate VSA tool, the predictive model could help to improve the safety of the endovascular repair procedure.

KEY POINTS

• Clinically useful prediction models for distal stent-induced new entry are still lacking, and the safety of the stent implantation is hard to guarantee. • Our proposed predictive tool based on a virtual stenting algorithm supports different stenting planning rehearsals and real-time risk evaluation, guiding clinicians to optimize the presurgical plan when necessary. • The established prediction model provides accurate risk evaluation for vessel damage, improving the safety of the intervention procedure.

Deployment of flow diverter devices: prediction of foreshortening and validation of the simulation in 18 clinical cases

PURPOSE

Flow diverter (FD) devices show severe shortening during deployment in dependency of the vessel geometry. Valid information regarding the geometry of the targeted vessel is therefore mandatory for correct device selection, and to avoid complications. But the geometry of diseased tortuous intracranial vessels cannot be measured accurately with standard methods. The goal of this study is to prove the accuracy of a novel virtual stenting method in prediction of the behavior of a FD in an individual vessel geometry.

METHODS

We applied a virtual stenting method on angiographic 3D imaging data of the specific vasculature of patients, who underwent FD treatment. The planning tool analyzes the local vessel morphology and deploys the FD virtually. We measured in 18 cases the difference between simulated FD length and real FD length after treatment in a landmark-based registration of pre-/post-interventional 3D angiographic datasets.

RESULTS

The mean value of length deviation of the virtual FD was 2.2 mm (SD ± 1.9 mm) equaling 9.5% (SD ± 8.2%). Underestimated cases present lower deviations compared with overestimated FDs. Flow diverter cases with a nominal device length of 20 mm had the highest prediction accuracy.

CONCLUSION

The results suggest that the virtual stenting method used in this study is capable of predicting FD length with a clinically sufficient accuracy in advance and could therefore be a helpful tool in intervention planning. Imaging data of high quality are mandatory, while processing and manipulation of the FD during the intervention may impact the accuracy.

Virtual stenting with simplex mesh and mechanical contact analysis for real-time planning of thoracic endovascular aortic repair

In aortic endovascular repair, the prediction of stented vessel remodeling informs treatment plans and risk evaluation; however, there are no highly accurate and efficient methods to quantitatively simulate stented vessels. This study developed a fast virtual stenting algorithm to simulate stent-induced aortic remodeling to assist in real-time thoracic endovascular aortic repair planning.

Methods: The virtual stenting algorithm was established based on simplex deformable mesh and mechanical contact analysis. The key parameters of the mechanical contact analysis were derived from mechanical tests on aortic tissue (n=40) and commonly used stent-grafts (n=6). Genetic algorithm was applied to select weighting parameters. Testing and validation of the algorithm were performed using pre- and post-treatment computed tomography angiography datasets of type-B aortic dissection cases (n=66).

Results: The algorithm was efficient in simulating stent-induced aortic deformation (mean computing time on a single processor: 13.78±2.80s) and accurate at the morphological (curvature difference: 1.57±0.57%; cross-sectional area difference: 4.11±0.85%) and hemodynamic (similarity of wall shear stress-derived parameters: 90.16-90.94%) levels. Stent-induced wall deformation was higher (p<0.05) in distal stent-induced new entry cases than in successfully treated cases, and this deformation did not differ significantly among the different stent groups. Additionally, the high stent-induced wall deformation regions and the new-entry sites overlapped, indicating the usefulness of wall deformation to evaluate the risks of device-induced complications.

Conclusion: The novel algorithm provided fast real-time and accurate predictions of stent-graft deployment with luminal deformation tracking, thereby potentially informing individualized stenting planning and improving endovascular aortic repair outcomes. Large, multicenter studies are warranted to extend the algorithm validation and determine stress-induced wall deformation cutoff values for the risk stratification of particular complications.