On geometric modeling of the human intracranial venous system

On presentation of cerebral arteries via cortical openings

The presentation of cortical arteries is challenging, as most of their course is hidden in the depth of the sulci. Despite that, demonstrating the arteries on the cortical surface is a standard way of their presentation. To keep advantages of surface presentation while lessening its limitation, we propose a novel context-related method of cerebrovasculature presentation by cortical openings consisting in the removal of a selected region from the cortical mantle and exposing underlying structures. We also introduce a reverse than standard vessel-to-context mapping from a gyrus/lobule to vessels supplying it.The method has the following steps: define a cortical opening, develop a tool to perform them, create cortical openings for gyri and lobules with underlying white matter and intracranial arteries, generate labeled and parcellated images for the created openings, and integrate the cortical opening images with the NOWinBRAIN public repository of 8600 3D neuroimages.The cortical openings are created for 64 gyri and six lobules for the left and right cerebral hemispheres resulting in 210 images arranged in triples as spatially corresponding non-parcellated and unlabeled, parcellated by color and unlabeled, and parcellated and labeled images.The cortical opening approach, generally, increases vessel exposure in a higher number of depicted branches, revealing arteries otherwise hidden deep in sulci, a more complete vessel course, and a lower number of required views.The gyrus/lobule-to-arteries mapping facilitates exploration of a studied region, encapsulates all local arteries, and reduces vascular complexity by decomposing the entire vascular system into smaller sets involved in the studied region.

Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system

Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices.

Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas

Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools.

Computational and mathematical methods in brain atlasing

Brain atlases have a wide range of use from education to research to clinical applications. Mathematical methods as well as computational methods and tools play a major role in the process of brain atlas building and developing atlas-based applications. Computational methods and tools cover three areas: dedicated editors for brain model creation, brain navigators supporting multiple platforms, and atlas-assisted specific applications. Mathematical methods in atlas building and developing atlas-aided applications deal with problems in image segmentation, geometric body modelling, physical modelling, atlas-to-scan registration, visualisation, interaction and virtual reality. Here I overview computational and mathematical methods in atlas building and developing atlas-assisted applications, and share my contribution to and experience in this field.

A shape-based inter-layer contours correspondence method for ICT-based reverse engineering

The correspondence of a stack of planar contours in ICT (industrial computed tomography)-based reverse engineering, a key step in surface reconstruction, is difficult when the contours or topology of the object are complex. Given the regularity of industrial parts and similarity of the inter-layer contours, a specialized shape-based inter-layer contours correspondence method for ICT-based reverse engineering was presented to solve the above problem based on the vectorized contours. In this paper, the vectorized contours extracted from the slices consist of three graphical primitives: circles, arcs and segments. First, the correspondence of the inter-layer primitives is conducted based on the characteristics of the primitives. Second, based on the corresponded primitives, the inter-layer contours correspond with each other using the proximity rules and exhaustive search. The proposed method can make full use of the shape information to handle industrial parts with complex structures. The feasibility and superiority of this method have been demonstrated via the related experiments. This method can play an instructive role in practice and provide a reference for the related research.

Segmentation and reconstruction of cerebral vessels from 3D rotational angiography for AVM embolization planning

Diagnosis and computer-guided therapy of cerebral Arterio-Venous Malformations (AVM) require an accurate understanding of the cerebral vascular network both from structural and biomechanical point of view. We propose to obtain such information by analyzing three Dimensional Rotational Angiography (3DRA) images. In this paper, we describe a two-step process allowing 1) the 3D automatic segmentation of cerebral vessels from 3DRA images using a region-growing based algorithm and 2) the reconstruction of the segmented vessels using the 3D constrained Delaunay Triangulation method. The proposed algorithm was successfully applied to reconstruct cerebral blood vessels from ten datasets of 3DRA images. This software allows the neuroradiologist to separately analyze cerebral vessels for pre-operative interventions planning and therapeutic decision making.

A variational surface deformation and subdivision-based modeling framework for noisy and small n-furcated tube-like structures

It is challenging to construct an accurate and smooth mesh for noisy and small n-furcated tube-like structures, such as arteries, veins, and pathological vessels, due to tiny vessel size, noise, n -furcations, and irregular shapes of pathological vessels. We propose a framework by dividing the modeling process into mesh construction and mesh refinement. In the first step, we focus on mesh topological correctness, and just create an initial rough mesh for the n-furcated tube-like structures. In the second step, we propose a variational surface deformation method to push the initial mesh to structure boundaries for positional accuracy improvement. By iteratively solving Euler-Lagrange equations derived from the minimization of the shell and distance energies, the initial mesh can be gradually pushed to the boundaries. A mesh dilation method is proposed to prevent the extremely deviated initial mesh moving toward wrong boundaries. We combine deformation and subdivision to propose a coarse-to-fine modeling framework for the improvement of efficiency and accuracy. Experiments show our method can construct an accurate and smooth mesh for noisy and small n-furcated tube-like structures, and it is useful in hemodynamics, quantitative measurement, and analysis of vessels.

Cardiovascular modeling of congenital heart disease based on neonatal echocardiographic images

This paper proposes a 3-D cardiovascular modeling system based on neonatal echocardiographic images. With the system, medical doctors can interactively construct patient-specific cardiovascular models, and share the complex topology and the shape information. For the construction of cardiovascular models with a variety of congenital heart diseases, we propose a set of algorithms and interface that enable editing of the topology and shape of the 3-D models. In order to facilitate interactivity, the centerline and radius of the vessels are used to edit the surface of the heart vessels. This forms a skeleton where the centerlines of blood vessel serve as the nodes and edges, while the radius of the blood vessel is given as an attribute value to each node. Moreover, parent-child relationships are given to each skeleton. They are expressed as the directed acyclic graph, where the skeletons are viewed as graph nodes and the connecting points are graph edges. The cardiovascular models generated from some patient data confirmed that the developed technique is capable of constructing cardiovascular disease models in a tolerable timeframe. It is successful in representing the important structures of the patient-specific heart vessels for better understanding in preoperative planning and electric medical recording of the congenital heart disease.

Postmortem image analysis of sheep cortical leptomeningeal space and vasculature: theoretical implications on brain surface dialysis

The vascular surface distribution of the subarachnoid space has not been studied extensively. The aim of our study was to develop a method of computer-assisted estimation of the distribution of the vascular network in the cortical leptomeninges and subarachnoid space and model it to aid the study of the physiology of brain surface dialysis. Nine sheets of leptomeningeal tissue were obtained from adult sheep. Fourteen image sample areas of 4 cm² each were acquired and processed with ImageJ. The vascular and nonvascular areas of the cortical subarachnoid space were identified using a "projected surface" approach. The modeling equations were used to predict the behavior of brain surface dialysis processes. The mean surface area of identified subarachnoid vessels was 0.354 ± 0.02 cm² per 1 cm² of tissue. The mean meningeal area with unidentified vessels was 0.646 ± 0.02 cm²/1 cm², and the difference between these surfaces was significant (p < 0.0001). The modeling equations used predict that modifying the vessel diameter of the subarachnoid space could alter the efficiency of brain surface dialysis. The computer-assisted modeling of the vascular surface of the cortical subarachnoid space may be a useful tool in depicting its morphology and assessing the physiology during brain surface dialysis.

Comparison of magnetic resonance angiography scans on 1.5, 3, and 7 Tesla units: a quantitative study of 3-dimensional cerebrovasculature

BACKGROUND

Although multiple studies demonstrate benefits of high field imaging of cerebrovasculature, a detailed quantitative analysis of complete cerebrovascular system is unavailable. To compare quality of MR angiography (MRA) acquisitions at various field strengths, we used 3-dimensional (3D) geometric cerebrovascular models extracted from 1.5 T/3 T/7 T scans.

METHODS

The 3D cerebrovascular models were compared in volume, length, and number of branches. A relationship between the vascular length and volume was statistically derived. Acquisition performance was benchmarked against the maximum volume at infinitive length.

RESULTS

The numbers of vessels discernible on 1.5 T/3 T/7 T are 138/363/907. 3T shows 3.3(1.9) and 7 T 1.2(9.1) times more arteries (veins) than 1.5 T. The vascular lengths and volumes at 1.5 T/3 T/7 T are 3.7/12.5/22.7 m and 15.8/26.6/28.0 cm(3). For arteries: 3T-1.5 T gain is very high in length, high in volume; 7 T-3T gain is medium in length, small in volume. For veins: 3 T-1.5 T gain is moderate in length, high in volume; 7 T-3T gain is very high in length, moderate in volume. 1.5 T shows merely half of vascular volume. At 3 T 6%, while at 7 T only 1% of vascular volume is missing.

CONCLUSION

Our approach differs from standard approaches based on visual assessment and signal (contrast)-to-noise ratio. It also measures absolute acquisition performance, provides a unique length-volume relationship, and predicts length/volume for intermediate teslages.

Vascular editor: from angiographic images to 3D vascular models

Modern imaging techniques are able to generate high-resolution multimodal angiographic scans. The analysis of vasculature using numerous 2D tomographic images is time consuming and tedious, while 3D modeling and visualization enable presentation of the vasculature in a more convenient and intuitive way. This calls for development of interactive tools facilitating processing of angiographic scans and enabling creation, editing, and manipulation of 3D vascular models. Our objective is to develop a vascular editor (VE) which provides a suitable environment for experts to create and manipulate 3D vascular models correlated with surrounding anatomy. The architecture, functionality, and user interface of the VE are presented. The VE includes numerous interactive tools for building a vascular model from multimodal angiographic scans, editing, labeling, and manipulation of the resulting 3D model. It also provides comprehensive tools for vessel visualization, correlation of 2D and 3D representations, and tracing of small vessels of subpixel size. Education, research, and clinical applications of the VE are discussed, including the atlas of cerebral vasculature. To our best knowledge, there are no other systems offering similar functionality as the VE does.

Computer-aided design of microvasculature systems for use in vascular scaffold production

In vitro biomedical engineering of intact, functional vascular networks, which include capillary structures, is a prerequisite for adequate vascular scaffold production. Capillary structures are necessary since they provide the elements and compounds for the growth, function and maintenance of 3D tissue structures. Computer-aided modeling of stereolithographic (STL) micro-computer tomographic (micro-CT) 3D models is a technique that enables us to mimic the design of vascular tree systems containing capillary beds, found in tissues. In our first paper (Mondy et al 2009 Tissue Eng. at press), using micro-CT, we studied the possibility of using vascular tissues to produce data capable of aiding the design of vascular tree scaffolding, which would help in the reverse engineering of a complete vascular tree system including capillary bed structures. In this paper, we used STL models of large datasets of computer-aided design (CAD) data of vascular structures which contained capillary structures that mimic those in the dermal layers of rabbit skin. Using CAD software we created from 3D STL models a bio-CAD design for the development of capillary-containing vascular tree scaffolding for skin. This method is designed to enhance a variety of therapeutic protocols including, but not limited to, organ and tissue repair, systemic disease mediation and cell/tissue transplantation therapy. Our successful approach to in vitro vasculogenesis will allow the bioengineering of various other types of 3D tissue structures, and as such greatly expands the potential applications of biomedical engineering technology into the fields of biomedical research and medicine.

Web-based interactive 3D visualization as a tool for improved anatomy learning

Despite a long tradition, conventional anatomy education based on dissection is declining. This study tested a new virtual reality (VR) technique for anatomy learning based on virtual contrast injection. The aim was to assess whether students value this new three-dimensional (3D) visualization method as a learning tool and what value they gain from its use in reaching their anatomical learning objectives. Several 3D vascular VR models were created using an interactive segmentation tool based on the "virtual contrast injection" method. This method allows users, with relative ease, to convert computer tomography or magnetic resonance images into vivid 3D VR movies using the OsiriX software equipped with the CMIV CTA plug-in. Once created using the segmentation tool, the image series were exported in Quick Time Virtual Reality (QTVR) format and integrated within a web framework of the Educational Virtual Anatomy (EVA) program. A total of nine QTVR movies were produced encompassing most of the major arteries of the body. These movies were supplemented with associated information, color keys, and notes. The results indicate that, in general, students' attitudes towards the EVA-program were positive when compared with anatomy textbooks, but results were not the same with dissections. Additionally, knowledge tests suggest a potentially beneficial effect on learning.

A new presentation and exploration of human cerebral vasculature correlated with surface and sectional neuroanatomy

The increasing complexity of human body models enabled by advances in diagnostic imaging, computing, and growing knowledge calls for the development of a new generation of systems for intelligent exploration of these models. Here, we introduce a novel paradigm for the exploration of digital body models illustrating cerebral vasculature. It enables dynamic scene compositing, real-time interaction combined with animation, correlation of 3D models with sectional images, quantification as well as 3D manipulation-independent labeling and knowledge-related meta labeling (with name, diameter, description, variants, and references). This novel exploration is incorporated into a 3D atlas of cerebral vasculature with arteries and veins along with the surrounding surface and sectional neuroanatomy derived from 3.0 Tesla scans. This exploration paradigm is useful in medical education, training, research, and clinical applications. It enables development of new generation systems for rapid and intelligent exploration of complicated digital body models in real time with dynamic scene compositing from highly parcellated 3D models, continuous navigation, and manipulation-independent labeling with multiple features.