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 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

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

Improved modelling of the human cerebral vasculature

Ph.DDOCTOR OF PHILOSOPH

Image-based versus atlas-based patient-specific S-value assessment for Samarium-153 EDTMP cancer palliative care: A short study

Introduction: Use of SPECT/CT data is the most accurate method for patient-specific internal dosimetry when isotopes emit single gamma rays. The manual or semi-automatic segmentation of organs is a major obstacle that slows down and limits the patient-specific dosimetry. Using digital phantoms that mimic patient�s anatomy can bypass the segmentation step and facilitate the dosimetry process. In this study, the results of a patient-specific dosimetry based on CT data and XCAT phantom, a flexible phantom with predefined organs, are compared. Methods: The dosimetry results (S-value and SAF) were calculated for a patient with breast cancer who received Samarium-153 ethylenediamine-N,N,N�,N�-tetrakis(methylenephosphonic acid (153Sm-EDTMP). Biodistribution of activity was obtained from the SPECT scan. The anatomical data and attenuation map were extracted from CT as well as the XCAT phantom with different BMIs. GATE Monte-Carlo simulator was used to calculate the dose to different organs based on the activity distribution and segmented anatomy. Results: The whole body dosimetry results are the same for both calculations based on the CT and XCAT with different BMIs; however for target organs, the differences between SAFs and S-values are high. In the spine, the clinically important target organ for Samarium therapy, the dosimetry results obtained from phantoms with unmatched BMIs between XCAT phantom and CT are substantially different. Conclusion: We showed that atlas-based dosimetry using XCAT phantom even with matched BMI may lead to considerable errors as compared to calculations based on patient�s own CT. For accurate dosimetry results, calculations should be done using CT data. © 2018 Iranian Journal of Nuclear Medicine. All Rights Reserved

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

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

Improved Image Guidance in TACE Procedures

Purpose of the work in this thesis is to improve the image guidance in TACE procedures. More specifically, we intend to develop and evaluate technology that permits dynamic roadmapping based on a 3D model of the liver vasculature