26 research outputs found
Accurate geometry reconstruction of vascular structures using implicit splines
3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy
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
Accurate geometry modeling of vasculatures using implicit fitting with 2D radial basis functions
Accurate vascular geometry modeling is an essential task in computer assisted vascular surgery and therapy. This paper presents a vessel cross-section based implicit vascular modeling technique, which represents a vascular surface as a set of locally fitted implicit surfaces. In the proposed method, a cross-section based technique is employed to extract from each cross-section of the vascular surface a set of points, which are then fitted with an implicit curve represented as 2D radial basis functions. All these implicitly represented cross-section curves are then being considered as 3D cylindrical objects and combined together using a certain partial shape-preserving spline to build a complete vessel branch; different vessel branches are then blended using a extended smooth maximum function to construct the complete vascular tree. Experimental results show that the proposed method can correctly represent the morphology and topology of vascular structures with high level of smoothness. Both qualitative comparison with other methods and quantitative validations to the proposed method have been performed to verify the accuracy and smoothness of the generated vascular geometric models
High-performance geometric vascular modelling
Image-based high-performance geometric vascular modelling and reconstruction is an essential component of computer-assisted surgery on the diagnosis, analysis and treatment of cardiovascular diseases. However, it is an extremely challenging task to efficiently reconstruct the accurate geometric structures of blood vessels out of medical images. For one thing, the shape of an individual section of a blood vessel is highly irregular because of the squeeze of other tissues and the deformation caused by vascular diseases. For another, a vascular system is a very complicated network of blood vessels with different types of branching structures. Although some existing vascular modelling techniques can reconstruct the geometric structure of a vascular system, they are either time-consuming or lacking sufficient accuracy. What is more, these techniques rarely consider the interior tissue of the vascular wall, which consists of complicated layered structures. As a result, it is necessary to develop a better vascular geometric modelling technique, which is not only of high performance and high accuracy in the reconstruction of vascular surfaces, but can also be used to model the interior tissue structures of the vascular walls.This research aims to develop a state-of-the-art patient-specific medical image-based geometric vascular modelling technique to solve the above problems. The main contributions of this research are:- Developed and proposed the Skeleton Marching technique to reconstruct the geometric structures of blood vessels with high performance and high accuracy. With the proposed technique, the highly complicated vascular reconstruction task is reduced to a set of simple localised geometric reconstruction tasks, which can be carried out in a parallel manner. These locally reconstructed vascular geometric segments are then combined together using shape-preserving blending operations to faithfully represent the geometric shape of the whole vascular system.- Developed and proposed the Thin Implicit Patch method to realistically model the interior geometric structures of the vascular tissues. This method allows the multi-layer interior tissue structures to be embedded inside the vascular wall to illustrate the geometric details of the blood vessel in real world
High precision implicit modeling for patient-specific coronary arteries
High precision geometric reconstruction of patient-specific coronary arteries plays a crucial role in visual diagnosis, treatment decision-making, and the evaluation of the therapeutic effect of interventions in coronary artery diseases. It is also a fundamental task and a basic requirement in the numerical simulation of coronary blood flow dynamics. In this paper, a new implicit modeling technique for the geometric reconstruction of patient-specific coronary arteries has been developed. In the proposed method, the coronary arteries geometry is reconstructed segment by segment using radial basis functions with ellipsoid constraint from the point cloud obtained with a volumetric vascular image segmentation method, and the individually reconstructed coronary branches are then combined using a shape-preserving implicit blending operation to form a complete coronary artery surface. The experiment results and validations indicate that the reconstructed vascular shapes are of high smoothness and faithfulness
Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation
Creating high-quality polygonal meshes which represent the membrane surface of neurons for both visualization and numerical simulation purposes is an important yet nontrivial task, due to their irregular and complicated structures. In this paper, we develop a novel approach of constructing a watertight 3D mesh from the abstract point-and-diameter representation of the given neuronal morphology. The membrane shape of the neuron is reconstructed by progressively deforming an initial sphere with the guidance of the neuronal skeleton, which can be regarded as a digital sculpting process. To efficiently deform the surface, a local mapping is adopted to simulate the animation skinning. As a result, only the vertices within the region of influence (ROI) of the current skeletal position need to be updated. The ROI is determined based on the finite-support convolution kernel, which is convolved along the line skeleton of the neuron to generate a potential field that further smooths the overall surface at both unidirectional and bifurcating regions. Meanwhile, the mesh quality during the entire evolution is always guaranteed by a set of quasi-uniform rules, which split excessively long edges, collapse undersized ones, and adjust vertices within the tangent plane to produce regular triangles. Additionally, the local vertices density on the result mesh is decided by the radius and curvature of neurites to achieve adaptiveness
Modeling and hexahedral meshing of cerebral arterial networks from centerlines
Computational fluid dynamics (CFD) simulation provides valuable information
on blood flow from the vascular geometry. However, it requires extracting
precise models of arteries from low-resolution medical images, which remains
challenging. Centerline-based representation is widely used to model large
vascular networks with small vessels, as it encodes both the geometric and
topological information and facilitates manual editing. In this work, we
propose an automatic method to generate a structured hexahedral mesh suitable
for CFD directly from centerlines. We addressed both the modeling and meshing
tasks. We proposed a vessel model based on penalized splines to overcome the
limitations inherent to the centerline representation, such as noise and
sparsity. The bifurcations are reconstructed using a parametric model based on
the anatomy that we extended to planar n-furcations. Finally, we developed a
method to produce a volume mesh with structured, hexahedral, and flow-oriented
cells from the proposed vascular network model. The proposed method offers
better robustness to the common defects of centerlines and increases the mesh
quality compared to state-of-the-art methods. As it relies on centerlines
alone, it can be applied to edit the vascular model effortlessly to study the
impact of vascular geometry and topology on hemodynamics. We demonstrate the
efficiency of our method by entirely meshing a dataset of 60 cerebral vascular
networks. 92% of the vessels and 83% of the bifurcations were meshed without
defects needing manual intervention, despite the challenging aspect of the
input data. The source code is released publicly
Computational methods to predict and enhance decision-making with biomedical data.
The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed
Vessel tractography using an intensity based tensor model
In the last decade, CAD (Coronary Artery Disease) has been the leading cause of death worldwide [1]. Extraction of arteries is a crucial step for accurate visualization, quantification, and tracking of pathologies. However, coronary artery segmentation is one of the most challenging problems in medical image analysis, since arteries are complex tubular structures with bifurcations, and have possible pathologies. Moreover, appearance of blood vessels and their geometry can be perturbed by stents, calcifications and pathologies such as stenosis. Besides, noise, contrast and resolution artifacts can make the problem more challenging. In this thesis, we present a novel tubular structure segmentation method based on an intensity-based tensor that fits to a vessel, which is inspired from diffusion tensor image (DTI) modeling. The anisotropic tensor inside the vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized with a single seed point and it is capable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrate the performance of our algorithm on 3 complex tubular structured synthetic datasets, and on 8 CTA (Computed Tomography Angiography) datasets (from Rotterdam Coronary Artery Algorithm Evaluation Framework) for quantitative validation. Additionally, extracted arteries from 10 CTA volumes are qualitatively evaluated by a cardiologist expert's visual scores