A low dimensional surrogate model for a fast estimation of strain in the thrombus during a thrombectomy procedure

Background: Intra-arterial thrombectomy is the main treatment for acute ischemic stroke due to large vessel occlusions and can consist in mechanically removing the thrombus with a stent-retriever. A cause of failure of the procedure is the fragmentation of the thrombus and formation of micro-emboli, difficult to remove. This work proposes a methodology for the creation of a low-dimensional surrogate model of the mechanical thrombectomy procedure, trained on realizations from high-fidelity simulations, able to estimate the evolution of the maximum first principal strain in the thrombus. Method: A parametric finite-element model was created, composed of a tapered vessel, a thrombus, a stentretriever and a catheter. A design of experiments was conducted to sample 100 combinations of the model parameters and the corresponding thrombectomy simulations were run and post-processed to extract the maximum first principal strain in the thrombus during the procedure. Then, a surrogate model was built with a combination of principal component analysis and Kriging. Results: The surrogate model was chosen after a sensitivity analysis on the number of principal components and was tested with 10 additional cases. The model provided predictions of the strain curves with correlation above 0.9 and a maximum error of 28%, with an error below 20% in 60% of the test cases. Conclusions: The surrogate model provides nearly instantaneous estimates and constitutes a valuable tool for evaluating the risk of thrombus rupture during pre-operative planning for the treatment of acute ischemic stroke.

Automated summary

This study proposes a low-dimensional surrogate model to estimate the evolution of maximum first principal strain in thrombus during the mechanical thrombectomy procedure. The surrogate model was built using a parametric finite-element model and validated with experimental simulations. The results show that the model provides highly correlated predictions with the actual strain curves and has a maximum error of 28% with an error below 20% in 60% of the test cases. The surrogate model is a valuable tool for assessing the risk of thrombus rupture during pre-operative planning for acute ischemic stroke treatment.