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

IMPACT OF HEMODYNAMIC VORTEX SPATIAL AND TEMPORAL CHARACTERISTICS ON ANALYSIS OF INTRACRANIAL ANEURYSMS

Subarachnoid hemorrhage is a potentially devastating pathological condition in which bleeding occurs into the space surrounding the brain. One of the prominent sources of subarachnoid hemorrhage are intracranial aneurysms (IA): degenerative, irregular expansions of area(s) of the cerebral vasculature. In the event of IA rupture, the resultant subarachnoid hemorrhage ends in patient mortality occurring in ~50% of cases, with survivors enduring significant neurological damage with physical or cognitive impairment. The seriousness of IA rupture drives a degree of clinical interest in understanding these conditions that promote both the development and possible rupture of the vascular malformations. Current metrics for the assessment of this pathology rely on measuring the geometric characteristics of a patient\u27s vessel and/or IA, as well as the hemodynamic stressors existing along the vessel wall. Comparatively less focus has been granted toward understanding the characteristics of much of the bulk-flow within the vasculature and how it may play a role in IAs. Specifically, swirling hemodynamic flow (vortices) have been suggested as a condition which exacerbates vascular changes leading to IAs, yet quantified measurements of the spatial and temporal characteristics of vortices remain overlooked. This dissertation studies the role of the spatial and temporal characteristics of vortex flow and how it plays a role on IA pathology. Its chapters are a collection of five (5) works into this matter. First, established methods for the identification of vortices was investigated, and a novel method for vortex identification and quantification of their characteristics was developed to overcome the limitations of previous methods. Second, the developed method for vortex identification/quantification was then applied to a simulation study to improve predictive models aimed at predicting areas of IA development from those unlikely to suffer this pathology. Third, assessing how the simulated repair of one IA impacts changes to hemodynamic conditions within other nearby un-repaired IAs in a multiple IA system. Fourth, it was determined if vortex identification/quantification improved predictive models aimed at differentiation ruptured from unruptured IAs. Fifth, impart vortical flow of differing characteristics onto cultured vascular cells to determine if vortex stability imparts varied levels of cellular changes

A novel procedure for medial axis reconstruction of vessels from Medical Imaging segmentation

A procedure for reconstructing the central axis from diagnostic image processing is presented here, capable of solving the widespread problem of stepped shape effect that characterizes the most common algorithmic tools for processing the central axis for diagnostic imaging applications through the development of an algorithm correcting the spatial coordinates of each point belonging to the axis from the use of a common discrete image skeleton algorithm. The procedure is applied to the central axis traversing the vascular branch of the cerebral system, appropriately reconstructed from the processing of diagnostic images, using investigations of the local intensity values identified in adjacent voxels. The percentage intensity of the degree of adherence to a specific anatomical tissue acts as an attraction pole in the identification of the spatial center on which to place each point of the skeleton crossing the investigated anatomical structure. The results were shown in terms of the number of vessels identified overall compared to the original reference model. The procedure demonstrates high accuracy margin in the correction of the local coordinates of the central points that permits to allocate precise dimensional measurement of the anatomy under examination. The reconstruction of a central axis effectively centered in the region under examination represents a fundamental starting point in deducing, with a high margin of accuracy, key informations of a geometric and dimensional nature that favours the recognition of phenomena of shape alterations ascribable to the presence of clinical pathologies

FLUID FLOW CHARACTERIZATION IN RAPID PROTOTYPED COMMON ILIAC ARTERY ANEURYSM MOLDS

The goal of this project was to determine whether i) fused deposition modeling could be employed to manufacture molds for vascular constructs, ii) whether vascular constructs could be created from these molds, and iii) to verify practical equivalence between observed fluid velocities. Dye tracking was to be employed to characterize fluid velocity profiles through the in vitro vascular constructs, including a half-vessel model and a full vessel model of an iliac artery aneurysm. A PDMS half-vessel construct was manufactured, and the movement of dye through the construct was tracked by a cellphone camera. Thresholds were applied to each video in HSB or YUV mode in ImageJ, and analyzed to determine the velocity of the fluid through the construct. COMSOL simulations of the half-vessel were conducted for comparison to the empirical observations. Plots describing the flow velocities along the maximum streamline path length were generated, and a one sample t-test was conducted at a 5% significance level to determine whether there was a significant difference between velocity values obtained by dye tracking and the COMSOL simulations. It was determined that the empirical dye tracking trials failed to demonstrate agreement between the measured and predicted flow rates. A full vessel construct was not completed due to unforeseen time constraints. Dye tracking was not determined to be reliable as a means of measuring the maximum velocity of fluid. Discrepancies between the empirical observations and the COMSOL simulation are discussed. The discrepancy was attributed to limitations in the experimental protocol; low frame rate, poor control over lighting conditions, and the subjectivity involved in image processing. Methods of improving upon the manufacturing and experimental protocols used for the half-vessel are proposed for future work, such as improving control over lighting conditions, choosing a camera with a higher frame rate, constructing a more stable fixture, exploring PIV. Additionally, the technical problems leading to the failure to complete the full vessel model are discussed, and changes in the manufacturing process are proposed to allow dissolution or removal of the aneurysm model

Doctor of Philosophy

dissertationHigh arterial tortuosity, or twistedness, is a sign of many vascular diseases. Some ocular diseases are clinically diagnosed in part by assessment of increased tortuosity of ocular blood vessels. Increased arterial tortuosity is seen in other vascular diseases but is not commonly used for clinical diagnosis. This study develops the use of existing magnetic resonance angiography (MRA) image data to study arterial tortuosity in a range of arteries of hypertensive and intracranial aneurysm patients. The accuracy of several centerline extraction algorithms based on Dijkstra's algorithm was measured in numeric phantoms. The stability of the algorithms was measured in brain arteries. A centerline extraction algorithm was selected based on its accuracy. A centerline tortuosity metric was developed using a curve of tortuosity scores. This tortuosity metric was tested on phantoms and compared to observer-based tortuosity rankings on a test data set. The tortuosity metric was then used to measure and compare with negative controls the tortuosity of brain arteries from intracranial aneurysm and hypertension patients. A Dijkstra based centerline extraction algorithm employing a distance-from-edge weighted center of mass (DFE-COM) cost function of the segmented arteries was selected based on generating 15/16 anatomically correct centerlines in a looping artery iv compared to 15/16 for the center of mass (COM) cost function and 7/16 for the inverse modified distance from edge cost function. The DFE-COM cost function had a lower root mean square error in a lopsided phantom (0.413) than the COM cost function (0.879). The tortuosity metric successfully ordered electronic phantoms of arteries by tortuosity. The tortuosity metric detected an increase in arterial tortuosity in hypertensive patients in 13/13 (10/13 significant at α = 0.05). The metric detected increased tortuosity in a subset of the aneurysm patients with Loeys-Dietz syndrome (LDS) in 7/7 (three significant at α = 0.001). The tortuosity measurement combination of the centerline algorithm and the distance factor metric tortuosity curve was able to detect increases in arterial tortuosity in hypertensives and LDS patients. Therefore the methods validated here can be used to study arterial tortuosity in other hypertensive population samples and in genetic subsets related to LDS

Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed

Novel methodologies for investigating the pathophysiology of cerebral aneurysms.

An intracranial aneurysm (IA) is a pathological state of a cerebral artery in which the elastin and smooth muscle cells found in the healthy arterial wall are absent. Rupture of an IA is a major cause of subarachnoid hemorrhage. Cerebral aneurysms are most commonly found at arterial bifurcations and the outer bends of curved vessels. The nature of blood flow in these regions is believed to play an important role in initiation, development and rupture of the IA. However, the coupling between hemodynamics and aneurysm pathophysiology is complex and remains poorly understood. The initiation of cerebral aneurysms is believed to be caused by a breakdown in the homeostatic mechanism of healthy arteries, leading to destructive wall remodeling and damage. Due to its complex nature, there is a need for both controlled in vitro and in vivo studies of IA initiation. We have designed an in vitro flow chamber that can be used to reproduce specific magnitudes of wall shear stress and wall shear stress gradients found at the apices of arterial bifurcations, where aneurysms tend to form. Particular attention is given to reproducing spatial distributions of these functions that have been shown to induce pre-aneurysmal changes in vivo. Animal models provide a mechanism for fundamental studies of the coupling between hemodynamics and pathophysiology in diseases such as saccular aneurysms. We conducted a sensitivity study to develop an accurate CFD model for an elastase induced rabbit aneurysm model. We then used this computational model to evaluate the capability of the rabbit model to reproduce hemodynamic features typical of human intracranial aneurysms. Geometric and hemodynamic features of 51 rabbit aneurysm models were analyzed and shown to fall within the range reported for human IAs. This model was also used to study the relationship between aspect ratio and hemodynamics in the aneurysm sac. An "in silico design" approach was then used to explore the possibility of extending the rabbit model to capture more of the flow categories identified in human IAs. Based on a previously developed parametric model for human arterial bifurcations, we created and validated a parametric model for intracranial aneurysms. This parametric model captures important geometric and flow features of both the aneurysm and neighboring vasculature. The model is currently being used for studies of the coupling between geometry and hemodynamics in intracranial aneurysms. It can also be used to guide 3D reconstruction of poor quality clinical data or construct in vitro experimental models