Semi-automatic measurements and description of the geometry of vascular tree based on Bézier spline curves: application to cerebral arteries

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.

Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data

Background

Aortic dissection (AD) is one of the most catastrophic aortic diseases associated with a high mortality rate. In contrast to the advances in most cardiovascular diseases, both the incidence and in-hospital mortality rate of AD have experienced deviant increases over the past 20 years, highlighting the need for fresh prospects on the prescreening and in-hospital treatment strategies.

Methods

Through two cross-sectional studies, we adopt image recognition techniques to identify pre-disease aortic morphology for prior diagnoses; assuming that AD has occurred, we employ functional data analysis to determine the optimal timing for BP and HR interventions to offer the highest possible survival rate.

Results

Compared with the healthy control group, the aortic centerline is significantly more slumped for the AD group. Further, controlling patients’ blood pressure and heart rate according to the likelihood of adverse events can offer the highest possible survival probability.

Conclusions

The degree of slumpness is introduced to depict aortic morphological changes comprehensively. The morphology-based prediction model is associated with an improvement in the predictive accuracy of the prescreening of AD. The dynamic model reveals that blood pressure and heart rate variations have a strong predictive power for adverse events, confirming this model’s ability to improve AD management.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13040-021-00249-8).

Age- and gender-dependent variability in the geometry of middle cerebral artery bifurcations

The goal of this study was to analyze trends in the geometric parameters of blood vessels with age in a randomly selected group of 200 computed tomography angiography studies of radiologically healthy individuals using dedicated statistical techniques. A quantitative description of cerebral vascular geometry with numerical parameters (bifurcation angle, branching angle, co-planarity index, average curvature, and average torsion) was proposed. The changes and variability in geometry were analyzed according to age. The bifurcation angle, branching angle, and average curvature increased with age, whereas the co-planarity index and average torsion decreased with age. For equal-diameter branches, women comparing to men presented lower bifurcation angles in the 4th decade of life, and lower values for the co-planarity index in the 3rd and 4th decades of life. In non-equal-diameter branches, the opposite relationship was observed for bifurcation angle in the 4th decade of life comparing both groups. All analyzed parameters showed clearly visible trends with patient age. Deviations in specific decades of life were detected; these deviations could be linked to the development of aneurysms in specific age groups.