A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively

Constrained estimation of intracranial aneurysm surface deformation using 4D-CTA

Background and objective Intracranial aneurysms are relatively common life-threatening diseases, and assessing aneurysm rupture risk and identifying the associated risk factors is essential. Parameters such as the Oscillatory Shear Index, Pressure Loss Coefficient, and Wall Shear Stress are reliable indicators of intracranial aneurysm development and rupture risk, but aneurysm surface irregular pulsation has also received attention in aneurysm rupture risk assessment. Methods The present paper proposed a new approach to estimate aneurysm surface deformation. This method transforms the estimation of aneurysm surface deformation into a constrained optimization problem, which minimizes the error between the displacement estimated by the model and the sparse data point displacements from the four-dimensional CT angiography (4D-CTA) imaging data. Results The effect of the number of sparse data points on the results has been discussed in both simulation and experimental results, and it shows that the proposed method can accurately estimate the surface deformation of intracranial aneurysms when using sufficient sparse data points. Conclusions Due to a potential association between aneurysm rupture and surface irregular pulsation, the estimation of aneurysm surface deformation is needed. This paper proposed a method based on 4D-CTA imaging data, offering a novel solution for the estimation of intracranial aneurysm surface deformation

In-silico clinical trials for assessment of intracranial flow diverters

In-silico trials refer to pre-clinical trials performed, entirely or in part, using individualised computer models that simulate some aspect of drug effect, medical device, or clinical intervention. Such virtual trials reduce and optimise animal and clinical trials, and enable exploring a wider range of anatomies and physiologies. In the context of endovascular treatment of intracranial aneurysms, in-silico trials can be used to evaluate the effectiveness of endovascular devices over virtual populations of patients with different aneurysm morphologies and physiologies. However, this requires (i) a virtual endovascular treatment model to evaluate device performance based on a reliable performance indicator, (ii) models that represent intra- and inter-subject variations of a virtual population, and (iii) creation of cost-effective and fully-automatic workflows to enable a large number of simulations at a reasonable computational cost and time. Flow-diverting stents have been proven safe and effective in the treatment of large wide-necked intracranial aneurysms. The presented thesis aims to provide the ingredient models of a workflow for in-silico trials of flow-diverting stents and to enhance the general knowledge of how the ingredient models can be streamlined and accelerated to allow large-scale trials. This work contributed to the following aspects: 1) To understand the key ingredient models of a virtual treatment workflow for evaluation of the flow-diverter performance. 2) To understand the effect of input uncertainty and variability on the workflow outputs, 3) To develop generative statistical models that describe variability in internal carotid artery flow waveforms, and investigate the effect of uncertainties on quantification of aneurysmal wall shear stress, 4) As part of a metric to evaluate success of flow diversion, to develop and validate a thrombosis model to assess FD-induced clot stability, and 5) To understand how a fully-automatic aneurysm flow modelling workflow can be built and how computationally inexpensive models can reduce the computational costs

Correlations Between Intracranial Aneurysms And Thoracic Aortic Aneurysms

This project investigates the clinical occurrence of concurrent thoracic aortic aneurysms (TAA) and intracranial aneurysms (ICA). We hypothesized that patients with a TAA have an increased risk of harboring a concurrent ICA, and likewise that patients with an ICA have an increased risk of harboring a concurrent TAA relative to the general population. In a separate arm of this project, we hypothesized that a pre-defined gene expression profile, based on the expression levels of 41 specific genes measured in peripheral blood cells, will exhibit a characteristic expression pattern in ICA patients and thereby have utility in detecting the presence of ICA. To accomplish the first objective of this project, we reviewed the charts of patients with TAA who also had recent intracranial imaging to document the prevalence of concurrent ICA and compared this rate to the ICA prevalence in the general population. Likewise, we reviewed the charts of patients with ICA who also had recent thoracic imaging to document the prevalence of concurrent TAA. To investigate the gene expression profile for detecting ICA, we collected peripheral blood samples from ICA patients and non- aneurysmal controls and measured the expression levels of 39 pre-defined genes in a signature aneurysm profile using real-time PCR. The observed pattern of expression of these genes was compared to a pre-defined signature aneurysm pattern to predict the aneurysm status of each sample. We found that 9.0% of 212 TAA patients we studied harbor a concurrent ICA. Patients with descending TAA and hypertension had significantly higher rates of concurrent ICA. We also found that 4.5% of 359 ICA patients we studied harbor a concurrent TAA. ICA patients over 70 years of age had an increased rate of concurrent TAA. We also analyzed gene expression in the blood samples of 17 ICA patients and 15 controls. By comparing the observed pattern of gene expression to a predefined signature aneurysm pattern, we were able to detect ICA from a peripheral blood test with an 88% sensitivity and overall accuracy of 63%. In conclusion, this project finds that patients with TAA are at an increased risk relative to the general population of harboring a concurrent ICA. Likewise, patients with ICA are at an increased risk relative to the general population of harboring a concurrent TAA. Our early results show that a peripheral blood test based on the gene expression pattern of 39 genes holds promise as a sensitive screening test for ICA

A workflow to generate physical 3D models of cerebral aneurysms applying open source freeware for CAD modeling and 3D printing

Objectives: 3D anatomical models are becoming a new frontier in surgery for planning and simulation on an individualized patient specific basis. Since 1999, 3D cerebral aneurysms models for neurosurgery have been proposed. The possibility of reproducing in a realistic 3D fashion the malformation with the surrounding vascular structures, provides important preoperative information for the treatment strategy. The same models can be used for training and teaching.Unfortunately stereolitography is often burdened by high costs and long times of production. These factors limit the possibility to use 3D models to plan surgeries in an easy daily fashion. Patients and methods: Our study enrolled 5 patients harboring cerebral aneurysms. DICOM data of each aneurysm were elaborated by an open source freeware to obtain CAD molds. Afterwards, the 3D models were produced using a fused deposition or a stereolitography printer. Results: Models were evaluated by Neurosurgeons in terms of quality and usefulness for surgical planning. Costs and times of production were recorded. Conclusions: Models were reliable, economically affordable and quick to produce. Keywords: Stereolitography, Cerebral aneurysms, 3D printing, Surgical planning, Aneurysm model

The Effect of Plasma Levels of Matrix Metalloproteinase-9 on Intracranial Aneurysm Progression

Intracranial aneurysms are a common vascular abnormality identified incidentally or after rupture leading to aneurysmal subarachnoid hemorrhage. However, accurately assessing the risk of aneurysmal disease progression is challenging. Here we propose a prospective cohort study to identify if elevated levels of matrix metalloproteinase-9, a molecular marker of vascular degeneration, in the peripheral blood samples of subjects with unruptured intracranial aneurysms correlate with aneurysm progression over a follow up period. This study aims to aid in identifying patients at increased risk of aneurysm progression in order to guide treatment decisions and ultimately decrease subarachnoid hemorrhage morbidity and mortality

Proposal for Numerical Benchmarking of Fluid-Structure Interaction in Cerebral Aneurysms

Computational fluid dynamics is intensively used to deepen the understanding of aneurysm growth and rupture in the attempt to support physicians during therapy planning. Numerous studies have assumed fully-rigid vessel walls in their simulations, whose sole hemodynamics may fail to provide a satisfactory criterion for rupture risk assessment. Moreover, direct in-vivo observations of intracranial aneurysm pulsation have been recently reported, encouraging the development of fluid-structure interaction for their modelling and for new assessments. In this work, we describe a new fluid-structure interaction benchmark setting for the careful evaluation of different aneurysm shapes. The studied configurations consist of three real aneurysm domes positioned on a toroidal channel. All geometric features, meshing characteristics, flow quantities, comparisons with a rigid-wall model and corresponding plots are provided. Reported results emphasize the alteration of flow patterns and hemodynamic descriptors when moving from the rigid-wall model to the complete fluid-structure interaction framework, thereby underlining the importance of the coupling between hemodynamics and the surrounding vessel tissue.Comment: 23 pages, 14 figure