Registration and Analysis of Vascular Images

We have developed a method for rigidly aligning images of tubes. This paper presents an evaluation of the consistency of that method for three-dimensional images of human vasculature. Vascular images may contain alignment ambiguities, poorly corresponding vascular networks, and non-rigid deformations, yet the Monte Carlo experiments presented in this paper show that our method provides registrations with sub-voxel consistency in less than one minute. Our registration method builds on the principals of our ridges-and-widths tube modeling work; this registration method operates by aligning models of the tubes in a source image with subsequent target images. The registration method’s consistency results from incorporate multi-scale ridge and width measures into the model-image match metric. The method’s speed comes from the use of coarse-to-fine registration strategies that are directly enabled by our tube models and the model-image match metric. In this paper we also show that the method’s insensitivity to local, non-rigid deformations enables the visualization and quantification of the effects of such deformations

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

VTrails: Inferring Vessels with Geodesic Connectivity Trees

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall

VTrails: Inferring vessels with geodesic connectivity trees

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall

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

Circle of Willis variants and cerebrovascular health: Representations, prevalences, functions and related consequences. Incomplete anatomy and changes to flow appear to induce more unfavourable health outcomes

Background: The Circle of Willis (CoW) is a circular structure of arteries in which most of the blood flowing to our brains pass through. The structure has primarily been regarded as important for its ability to redistribute blood flow in case of acute arterial occlusion, but may also have a role in dampening the pressure gradient in cerebral blood flow. The CoW anatomy also varies considerably, where its segments can be missing or thinner than normal, and therefore appears as a risk factor for cerebrovascular health. Objectives: To describe and report (I) the observed CoW variants and anatomy, and also examine the incomplete CoW variants’ associations to (II) white matter hyperintensities (WMH) and (III) saccular intracranial aneurysms (IA) compared to the complete CoW variant. Methods: Participants were invited from The Seventh Tromsø Study of which 1878 underwent magnetic resonance imaging. From the scans, CoW variants were semiautomatically classified. Likewise, WMH was automatically segmented and IAs were manually ascertained by radiologists. Results: The complete CoW is not very prevalent in participants older than 40 years old, and our findings suggest that the CoW becomes more incomplete with older age. Furthermore, incomplete CoW variants were not associated with increased WMH volume compared to the complete CoW variant. Incomplete CoW variants were associated increased odds of IA presence compared to the complete CoW variant. Conclusion: The results indicate that a complete CoW variant is not common in adults and elderly, which may have unfortunate consequences when incomplete CoW variants are associated with increased prevalence of IAs. Fortunately, not all results imply unfavourable outcomes, but further study of the CoW changes and possible effects of the variants over time are required.Bakgrunn: Willis Sirkel (CoW) er en sirkulær struktur av arterier i bunnen av hjernen som det meste av blodet går igjennom på tur til hjernen. Strukturen har vært antatt viktig for dens evner til å omdisponere blod i tilfellet arterier går tett, men i nyere tid har det også blitt foreslått at strukturen kan være viktig for å dempe pulstrykket i hjernen fra hjertet. Anatomien til CoW varierer mye, der segmenter mangler eller er tynnere enn normalt, og framstår dermed som et mulig risikomoment for hjernehelsen. Mål: Å beskrive (I) CoW varianter og anatomi. Analysere ufullstendige CoW varianters assosiasjoner til (II) vevsskader i hjernens indre som kalles hvit materie hyperintensiteter (WMH) og (III) sakkulære intrakranielle aneurismer (IA) sammenliknet med den fullstendige CoW varianten. Metoder: Deltakere ble invitert fra den Syvende Tromsøundersøkelsen hvorav 1878 ble tatt hjernebilder av med magnetresonans. Fra disse bildene ble CoW anatomi klassifisert. Likeså ble WMH automatisk segmentert og IA påvist av radiologer. Resultater: Den fullstendige CoW var ikke vanlig blant deltakerne eldre enn 40 år, og vi observerte også at CoW anatomien ble mer og mer ufullstendig hos eldre. Videre var ufullstendige CoW varianter ikke assosiert med høyere forekomst av WMH sammenliknet med den fullstendige CoW. Videre var ufullstendige CoW varianter assosiert med forhøyet odds for å ha IA sammenliknet med den fullstendige CoW. Konklusjon: Resultatene antyder at en fullstendig CoW ikke er spesielt vanlig hos voksne og eldre, noe som kan få uheldige følger når ufullstendige CoW er assosiert med økt forekomst av IA. Heldigvis antyder ikke alle resultatene negative følger, men mer forskning på CoW endringer og mulige effekter av anatomien over tid behøves for å stadfeste resultatene

An insight into the brain of patients with type-2 diabetes mellitus and impaired glucose tolerance using multi-modal magnetic resonance image processing

The purpose of this thesis was to investigate brain anatomy and physiology of subjects with impaired glucose tolerance (IGT - 12 subjects), type-2 diabetes (T2DM - 17 subjects) and normoglycemia (16 subjects) using multi-modal magnetic resonance imaging (MRI) at 3T. Perfusion imaging using quantitative STAR labeling of arterial regions (QUASAR) arterial spin labeling (ASL) was the core dataset. Optimization of the post-processing methodology for this sequence was performed and the outcome was used for hemodynamic analysis of the cohort. Typical perfusion-related parameters, along with novel hemodynamic features were quantified. High-resolution structural, angiographic and carotid flow scans were also acquired and processed. Functional acquisitions were repeated following a vasodilating stimulus. Differences between the groups were examined using statistical analysis and a machine-learning framework. Hemodynamic parameters differing between the groups emerged from both baseline and post-stimulus scans for T2DM and mainly from the post-stimulus scan for IGT. It was demonstrated that quantification of not-typically determined hemodynamic features could lead to optimal group-separation. Such features captured the pattern of delayed delivery of the blood to the arterial and tissue compartments of the hyperglycemic groups. Alterations in gray and white matter, cerebral vasculature and carotid blood flow were detected for the T2DM group. The IGT cohort was structurally similar to the healthy cohort but demonstrated functional similarities to T2DM. When combining all extracted MRI metrics, features driving optimal separation between different glycemic conditions emerged mainly from the QUASAR scan. The only highly discriminant non-QUASAR feature, when comparing T2DM to healthy subjects, emerged from the cerebral angiogram. In this thesis, it was demonstrated that MRI-derived features could lead to potentially optimal differentiation between normoglycemia and hyperglycemia. More importantly, it was shown that an impaired cerebral hemodynamic pattern exists in both IGT and T2DM and that the IGT group exhibits functional alterations similar to the T2DM group

EVALUATION OF EARLY TUMOR ANGIOGENESIS USING ULTRASOUND ACOUSTIC ANGIOGRAPHY

Cancer angiogenesis is a feature of tumor growth that produces disorganized and dysfunctional vascular networks. Acoustic angiography is a unique implementation of contrast-enhanced ultrasound that allows us to visualize microvasculature with high resolution and contrast, including blood vessels as small as 100 to 150 micrometers. These angiography images can be analyzed to evaluate the morphology of the blood vessels for the purpose of detecting and diagnosing tumors. This thesis describes the implementation, advantages, and disadvantages of acoustic angiography and evaluates tumor vasculature in a pre-clinical cancer model. Measurements of tortuosity and vascular density in tumor regions were significantly higher than those of control regions, including in the smallest palpable tumors (2-3 mm). Additionally, abnormal tortuosity extended beyond the margin of tumors, as distal tissue separated from the tumor by at least 4 mm exhibited higher tortuosity than healthy individuals. Vascular tortuosity was negatively correlated to distance from the tumor margin using linear regression. Analysis of full images to detect tumors was performed using a reader study approach to assess visual interpretations, and quantitative analysis combined tortuosity with spatial relationships between vessels using a density-based clustering approach. Visual assessment using a reader study design resulted in an area under the receiver operating characteristic (ROC) curve of approximately 0.8, and the ROC curve was significantly correlated with tumor diameter, indicating that larger tumors were detected more accurately using this approach. Quantitative analysis of the same images used a density-based clustering algorithm to combine vessels in an image into clusters based on their tortuosity (using 2 metrics), radius, and proximity to one another. In tumors, highly tortuous vessels were closely packed, forming large clusters in the analysis, while control images lacked such patterns and formed much smaller clusters. Therefore, maximum cluster size was used to detect tumors, achieving an area under the ROC curve of 0.96. Finally, superharmonic molecular imaging was used to image targeted microbubbles with higher contrast to tissue ratios than conventional molecular imaging. These molecular images were combined with vascular acoustic angiography images to begin to relate the expression of endothelial markers of angiogenesis with vascular features such as tortuosity.Doctor of Philosoph

Trends in Cerebrovascular Surgery and Interventions

This is an open access proceeding book of 9th European-Japanese Cerebrovascular Congress at Milan 2018. Since many experts from Europe and Japan had very important and fruitful discussion on the management of Cerebrovascular diseases, the proceeding book is very attractive for the physician and scientists of the area