3D reconstruction of cerebral blood flow and vessel morphology from x-ray rotational angiography

Three-dimensional (3D) information on blood flow and vessel morphology is important when assessing cerebrovascular disease and when monitoring interventions. Rotational angiography is nowadays routinely used to determine the geometry of the cerebral vasculature. To this end, contrast agent is injected into one of the supplying arteries and the x-ray system rotates around the head of the patient while it acquires a sequence of x-ray images. Besides information on the 3D geometry, this sequence also contains information on blood flow, as it is possible to observe how the contrast agent is transported by the blood. The main goal of this thesis is to exploit this information for the quantitative analysis of blood flow. I propose a model-based method, called flow map fitting, which determines the blood flow waveform and the mean volumetric flow rate in the large cerebral arteries. The method uses a model of contrast agent transport to determine the flow parameters from the spatio-temporal progression of the contrast agent concentration, represented by a flow map. Furthermore, it overcomes artefacts due to the rotation (overlapping vessels and foreshortened vessels at some projection angles) of the c-arm using a reliability map. For the flow quantification, small changes to the clinical protocol of rotational angiography are desirable. These, however, hamper the standard 3D reconstruction. Therefore, a new method for the 3D reconstruction of the vessel morphology which is tailored to this application is also presented. To the best of my knowledge, I have presented the first quantitative results for blood flow quantification from rotational angiography. Additionally, the model-based approach overcomes several problems which are known from flow quantification methods for planar angiography. The method was mainly validated on images from different phantom experiments. In most cases, the relative error was between 5% and 10% for the volumetric mean flow rate and between 10% and 15% for the blood flow waveform. Additionally, the applicability of the flow model was shown on clinical images from planar angiographic acquisitions. From this, I conclude that the method has the potential to give quantitative estimates of blood flow parameters during cerebrovascular interventions

Morphology parameters for intracranial aneurysm rupture risk assessment

OBJECTIVE—The aim of this study is to identify image-based morphological parameters that correlate with human intracranial aneurysm (IA) rupture. METHODS—For 45 patients with terminal or sidewall saccular IAs (25 unruptured, 20 ruptured), three-dimensional geometries were evaluated for a range of morphological parameters. In addition to five previously studied parameters (aspect ratio, aneurysm size, ellipticity index, nonsphericity index, and undulation index), we defined three novel parameters incorporating the parent vessel geometry (vessel angle, aneurysm [inclination] angle, and [aneurysm-to-vessel] size ratio) and explored their correlation with aneurysm rupture. Parameters were analyzed with a two-tailed independent Student's t test for significance; significant parameters (P < 0.05) were further examined by multivariate logistic regression analysis. Additionally, receiver operating characteristic analyses were performed on each parameter. RESULTS—Statistically significant differences were found between mean values in ruptured and unruptured groups for size ratio, undulation index, nonsphericity index, ellipticity index, aneurysm angle, and aspect ratio. Logistic regression analysis further revealed that size ratio (odds ratio, 1.41; 95% confidence interval, 1.03−1.92) and undulation index (odds ratio, 1.51; 95% confidence interval, 1.08−2.11) had the strongest independent correlation with ruptured IA. From the receiver operating characteristic analysis, size ratio and aneurysm angle had the highest area under the curve values of 0.83 and 0.85, respectively. CONCLUSION—Size ratio and aneurysm angle are promising new morphological metrics for IA rupture risk assessment. Because these parameters account for vessel geometry, they may bridge the gap between morphological studies and more qualitative location-based studies

FUSION IMAGE AND IA ICG IN AVM SURGERY

Objective: An understanding of the complex morphology of an arteriovenous malformation (AVM) is important for successful resection. We have previously reported the utility of intra-arterial indocyanine green (ICG) videoangiography for this purpose, but that method cannot detect the angioarchitecture covered by brain tissue. 3-dimensional (3D) multimodal fusion imaging is reportedly useful for this same purpose, but cannot always visualize the exact angioarchitecture due to poor source images and processing techniques. This study examined the results of utilizing both techniques in patients with AVMs. Methods: Both techniques were applied in 12 patients with AVMs. Both images were compared with surgical views and evaluated by surgeons. Results: Although evaluations for identifying superficial feeders by ICG videoangiography were high in all cases, the more complicated the AVM, the lower the evaluation by 3D multimodal fusion imaging. Conversely, evaluation of the estimated range of the nidus was high in all cases by 3D multimodal fusion imaging, but low in all but one case by ICG videoangiography. Nidus flow reduction was recognized by Flow 800 analysis obtained after ICG videoangiography. Conclusions: These results showed that utilizing both techniques together was more useful than each modality alone in AVM surgery. This was particularly effective in identifying superficial feeders and estimating the range of the nidus. This technique is expected to offer an optimal tool for AVM surgery

Silicone models as basic training and research aid in endovascular neurointervention-a single-center experience and review of the literature

The rapid development and wider use of neurointerventional procedures have increased the demand for a comprehensive training program for the trainees, in order to safely and efficiently perform these procedures. Artificial vascular models are one of the dynamic ways to train the new generation of neurointerventionists to acquire the basic skills of material handling, tool manipulation through the vasculature, and development of hand-eye coordination. Herein, the authors present their experience regarding a long-established training program and review the available literature on the advantages and disadvantages of vascular silicone model training. Additionally, they present the current research applications of silicone replicas in the neurointerventional arena

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

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Silicone models as basic training and research aid in endovascular neurointervention—a single-center experience and review of the literature

The rapid development and wider use of neurointerventional procedures have increased the demand for a comprehensive training program for the trainees, in order to safely and efficiently perform these procedures. Artificial vascular models are one of the dynamic ways to train the new generation of neurointerventionists to acquire the basic skills of material handling, tool manipulation through the vasculature, and development of hand-eye coordination. Herein, the authors present their experience regarding a long-established training program and review the available literature on the advantages and disadvantages of vascular silicone model training. Additionally, they present the current research applications of silicone replicas in the neurointerventional arena