Computational Methods Of Modeling Vascular Geometry And Tracking Pulmonary Motion From Medical Images

Modern anatomical medical imaging technologies, such as computed tomography and magnetic resonance, capture structures of the human body in exquisite detail. Computational anatomy is a developing discipline to extract and characterize the anatomy from images. Unfortunately, anatomical images do not reveal the functional behavior. Computational physiology shows great potential to link the structure-function relationship by considering both the anatomical information and the physical governing laws. The simulated physiology can be used to assess physiological states, and more importantly predict the outcomes of interventions. On the other hand, advances in the functional imaging techniques provide measured physiology information and should be utilized together with computational physiology. In the theme of computational anatomy and physiology, this dissertation describes computational methods of modeling vascular geometry for image-based blood flow computation and tracking pulmonary motion for image-guided radiation therapy. Blood flow computation is a useful tool to quantify in vivo hemodynamics. The essential first step is to model vascular geometry from medical imaging data. I have developed a new workflow for this task. The geometric model construction is based on 3D image segmentation and geometric processing. To represent the topology of the constructed model, I have developed a novel centerline extraction method. To account for compliant vessels, methods to assign spatially-varying mechanical properties of the vessel wall are also developed. The workflow greatly increases the modeling efficiency. The combination of the patient-specific geometry and wall deformation can enhance the fidelity of blood flow simulation. Image-based blood flow computation also holds great promise for device design and surgical procedure evaluation. Next, I have developed novel virtual intervention methods to deploy stents or stent grafts to patient-specific pre-operative geometric models constructed from medical images. These methods enable prospective model construction and may be used to evaluate the outcomes of alternative treatment options. Respiratory motion is closely related to the physiology of the lung. Finally, I have developed a novel framework to track patient-specific pulmonary motion from 4D computed tomography images. A large set of vascular junction structures in the lung are identified as landmarks and tracked to obtain their motion trajectories. This framework can provide accurate motion information, which is important in radiation therapy to reduce healthy tissue irradiation while allowing target dose escalation. This work demonstrates the importance of the geometry and motion modeling tools in computational anatomy and physiology. Accurate physiological information, whether simulated or measured, will benefit the diagnosis and treatment of various diseases.

Modern anatomical medical imaging technologies, such as computed tomography and magnetic resonance, capture structures of the human body in exquisite detail. Computational anatomy is a developing discipline to extract and characterize the anatomy from images. Unfortunately, anatomical images do not reveal the functional behavior. Computational physiology shows great potential to link the structure-function relationship by considering both the anatomical information and the physical governing laws. The simulated physiology can be used to assess physiological states, and more importantly predict the outcomes of interventions. On the other hand, advances in the functional imaging techniques provide measured physiology information and should be utilized together with computational physiology. In the theme of computational anatomy and physiology, this dissertation describes computational methods of modeling vascular geometry for image-based blood flow computation and tracking pulmonary motion for image-guided radiation therapy. Blood flow computation is a useful tool to quantify in vivo hemodynamics. The essential first step is to model vascular geometry from medical imaging data. I have developed a new workflow for this task. The geometric model construction is based on 3D image segmentation and geometric processing. To represent the topology of the constructed model, I have developed a novel centerline extraction method. To account for compliant vessels, methods to assign spatially-varying mechanical properties of the vessel wall are also developed. The workflow greatly increases the modeling efficiency. The combination of the patient-specific geometry and wall deformation can enhance the fidelity of blood flow simulation. Image-based blood flow computation also holds great promise for device design and surgical procedure evaluation. Next, I have developed novel virtual intervention methods to deploy stents or stent grafts to patient-specific pre-operative geometric models constructed from medical images. These methods enable prospective model construction and may be used to evaluate the outcomes of alternative treatment options. Respiratory motion is closely related to the physiology of the lung. Finally, I have developed a novel framework to track patient-specific pulmonary motion from 4D computed tomography images. A large set of vascular junction structures in the lung are identified as landmarks and tracked to obtain their motion trajectories. This framework can provide accurate motion information, which is important in radiation therapy to reduce healthy tissue irradiation while allowing target dose escalation. This work demonstrates the importance of the geometry and motion modeling tools in computational anatomy and physiology. Accurate physiological information, whether simulated or measured, will benefit the diagnosis and treatment of various diseases.

Image-Based Computational Modeling of the Human Circulatory and Pulmonary Systems provides an overview of the current modeling methods and applications enhancing interventional treatments and computer-aided surgery. A detailed description of the techniques behind image acquisition, processing and three-dimensional reconstruction are included. Techniques for the computational simulation of solid and fluid mechanics and structure interaction are also discussed, in addition to various cardiovascular and pulmonary applications. Engineers and researchers involved with image processing and computational modeling of human organ systems will find this a valuable reference.

This book discusses geometric and mathematical models that can be used to study fluid and structural mechanics in the cardiovascular system. Where traditional research methodologies in the human cardiovascular system are challenging due to its invasive nature, several recent advances in medical imaging and computational fluid and solid mechanics modelling now provide new and exciting research opportunities. This emerging field of study is multi-disciplinary, involving numerical methods, computational science, fluid and structural mechanics, and biomedical engineering. Certainly any new student or researcher in this field may feel overwhelmed by the wide range of disciplines that need to be understood. This unique book is one of the first to bring together knowledge from multiple disciplines, providing a starting point to each of the individual disciplines involved, attempting to ease the steep learning curve. This book presents elementary knowledge on the physiology of the cardiovascular system; basic knowledge and techniques on reconstructing geometric models from medical imaging; mathematics that describe fluid and structural mechanics, and corresponding numerical/computational methods to solve its equations and problems. Many practical examples and case studies are presented to reinforce best practice guidelines for setting high quality computational models and simulations. These examples contain a large number of images for visualization, to explain cardiovascular physiological functions and disease. The reader is then exposed to some of the latest research activities through a summary of breakthrough research models, findings, and techniques. The book’s approach is aimed at students and researchers entering this field from engineering, applied mathematics, biotechnology or medicine, wishing to engage in this emerging and exciting field of computational hemodynamics modelling.

With the development of medical imaging and numerical simulation techniques, image based Patient Specific Computational Hemodynamics (PSCH) has become a powerful approach to non-invasively quantify vascular fluid mechanics in human arteries. However, most existing PSCH methods are currently impractical for real clinic applications due to time consuming computation. In clinic applications, an image based efficient PSCH approach is needed to offer Patient-Specific diagnosis information in a timely manner and also to make large population studies possible. Without these studies, the correlation between Patient-Specific clinical symptom and hemodynamic patterns cannot be assessed. In the image based PSCH processing procedure, there are three main parts responsible for the bulk of the computation. The first one is obtaining the anatomic geometry. The second is converting the geometry into a suitable mesh or grid for simulation. The third hotspot is the large numbers of calculations in hemodynamics simulation.To address above problems, in this thesis, some efficient algorithms have been proposed to speed up image based PSCH computations.First, a fully parallel numerical method is proposed to quickly extract anatomical geometry from medical images. This method can efficiently solve the level set equations of active contour based on Lattice Boltzmann model (LBM) for image segmentation. And a parallel distance field regularization algorithm is integrated to the LBM computing scheme to keep computation stable. This approach avoids external regularization which has been a major impediment to direct parallelization of level set evolution with LBM. It allows the whole computing process to be efficiently executed on Graphics Processing Unit (GPU). Further, this method can be incorporated with different image features for various image segmentation tasks.Second, a new method is proposed by utilizing fluid features, particularly the mean flow intensity, to extract the blood flow field in a target vessel from 4D flow MRI images which encode blood velocity information in vessels. This approach is computational efficient and robust to blood flow changes in a cardiac cycle even in relatively small arteries. The extracted velocity field can be used as inlet and outlet conditions facilitating the hemodynamics simulation and for evaluating simulation result. Moreover, the target artery wall deformation factors at a few spatial locations of the artery and time steps in one cardiac cycle can be estimated. These factors enable the generation of high quality and deforming artery walls by morphing the wall acquired from accurate but static Time of Flight (TOF) MRI images. The dynamic artery wall benefits blood flow visualization tasks and also has potential to be used as moving boundary for hemodynamic simulations.Third, after image segmentation, the anatomical geometry is implicitly represented by zero level set of distance field. Based on segmentation result, a method is first proposed to directly generate simulation grids for Volumetric Lattice Boltzmann Method (VLBM) based hemodynamic simulation. There is no surface reconstruction and mesh generation needed. Therefore, it can avoid extra computational cost and inaccuracy during the two transforms: from volume to mesh, then from mesh to volume grids. Further, surface normal direction at artery wall can also be directly estimated to calculate Wall Shear Stress (WSS) .Finally, VLBM has been GPU accelerated to simulate hemodynamics in human arteries by using a uniform computing scheme for both fluid and boundary grids. For traditional computational fluid simulation method, its boundary conditions have to be separately performed over boundary nodes from fluid nodes, where large number of branching operations are inefficient for GPU parallel computation due to its Single Instruction Multiple Data (SIMD) architecture. For complicated biomechanics structure, this situation would be worse, as there are a lot of boundary cells in the computation domain. The proposed parallel VLBM implementation does not need to distinguish fluid and boundary cells in the computation so that branching is minimized and the GPU kernel execution is accelerated.

The two-volume set LNCS 5761 and LNCS 5762 constitute the refereed proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, held in London, UK, in September 2009. Based on rigorous peer reviews, the program committee carefully selected 259 revised papers from 804 submissions for presentation in two volumes. The first volume includes 125 papers divided in topical sections on cardiovascular image guided intervention and robotics; surgical navigation and tissue interaction; intra-operative imaging and endoscopic navigation; motion modelling and image formation; image registration; modelling and segmentation; image segmentation and classification; segmentation and atlas based techniques; neuroimage analysis; surgical navigation and robotics; image registration; and neuroimage analysis: structure and function.

Acute pulmonary embolism (APE) has a high mortality and many cases of APE go undiagnosed, as the pulmonary circulation is relatively hidden from clinical examination. The pathophysiology of APE is not completely understood, as there is a complex interplay of mechanisms that contribute to the disorder’s response. A difficulty in treating APE is that the mechanisms contributing to response are not well defined, and therefore it is difficult to predict which patients will respond most sensitively to a given clot load based on clinical evidence. Insight into the mechanisms of APE progression and severity has relied on controlled animal studies. Pigs are a widely-used experimental animal for representing human physiology and pathophysiology, because their comparative anatomy, as well as physiological and pathophysiological responses, are said to closely resemble that of humans. However, differences between pig and human in size and lung anatomy leads to translational limitations that are sometimes overlooked. Computational models with appropriate validation could bridge the gap in translating data from animal studies to human clinical practice. In the area of APE this translation is currently limited by a lack of a validated structure-function model for perfusion of the porcine lung. The branching geometry of the pulmonary arterial and venous trees in pig is different in structure to the human pulmonary vasculature, and studies have previously suggested that species-specific branching asymmetry of the pulmonary blood vessels contributes to differences observed in pulmonary blood flow distribution between species. A realistic model that accurately reflects the geometry and mechanical properties of the in vivo porcine lung is therefore critical for translating detailed investigation of structurefunction relationships in the pulmonary circulation of the pig to human. The overall aim of this research was to develop a novel, validated computational model for the porcine pulmonary circulation, that can be used to understand the interplay between the fundamental mechanisms of pulmonary vascular disease. A structure-based theoretical model that integrates new imaging and experimental data, plus previous experimental and clinical observations, is presented here. This thesis presents a quantitative analysis of the pulmonary arteries in five pig lungs, characterising their branching pattern, inter-subject similarity, and self-similarity in branching geometry. A summary model for the self-similar pulmonary arterial tree is described. A method for generating anatomically-based finite element models of the porcine pulmonary vascular tree was developed, based on previous volume-filling branching methods and the new knowledge of the porcine pulmonary arterial tree morphometry. Subject-specific spatially distributed models were generated for each animal using this new method (in the prone posture, at close to full lung expansion), and the full pulmonary arterial tree geometry statistics were compared with experimental data from the five animals. The generated models were consistent with the data with respect to key morphometric parameters of branching angles, rates of reduction of branch diameter and length with branch order, rate of increase of number of branches in an order with reduction in order, ratios of minor or major child diameters to parent diameter, and length to diameter ratios. A multi-scale model was implemented to simulate the distribution of perfusion in the porcine lung. The model includes an approximation for the deformation of the lung tissue due to change in lung size and posture. Model predictions for the lung supine, at close to functional residual capacity, compared well with the haemodynamic data from each animal at baseline. The performance of the model was assessed for predicting haemodynamics and gas exchange following arterial occlusion in APE. The model predicted the general trends of the experimental data, but was not completely consistent with regional functional imaging. The model also suggested that recruitment of small vessels (arterio-venous shunts, or supernumerary vessels) could be important for mitigating increase in pulmonary vascular resistance when the proportion of occluded lung increases. An important question was whether a subject-specific model is necessary for all studies, or whether a single (generic) geometry with appropriate boundary conditions is sufficient to reproduce the important behaviours of the pulmonary circulation. A generic species-specific model was therefore developed and validated, by demonstrating that any subject-specific porcine model can be parameterised to reflect individual pulmonary vascular function that has been measured for any other subject. The model was extended further by including a model for hypoxic pulmonary vasoconstriction. Simulation of normoxic and hypoxic ventilation was compared against experimental data from an independent study. The model prediction of arterial constriction during hypoxia (indicated by elevation of pulmonary artery pressure) and change in blood gases from normoxia were consistent with experiment. This research has established a new validated model to complement animal experimental studies, such that the interaction of mechanisms that contribute to APE can be investigated and presented in a quantitative way.

The sixteen chapters included in this book were written by invited experts of international recognition and address important issues in Medical Image Processing and Computational Vision, including: Object Recognition, Object Detection, Object Tracking, Pose Estimation, Facial Expression Recognition, Image Retrieval, Data Mining, Automatic Video Understanding and Management, Edges Detection, Image Segmentation, Modelling and Simulation, Medical thermography, Database Systems, Synthetic Aperture Radar and Satellite Imagery. Different applications are addressed and described throughout the book, comprising: Object Recognition and Tracking, Facial Expression Recognition, Image Database, Plant Disease Classification, Video Understanding and Management, Image Processing, Image Segmentation, Bio-structure Modelling and Simulation, Medical Imaging, Image Classification, Medical Diagnosis, Urban Areas Classification, Land Map Generation. The book brings together the current state-of-the-art in the various multi-disciplinary solutions for Medical Image Processing and Computational Vision, including research, techniques, applications and new trends contributing to the development of the related areas.

The Handbook of Medical Image Processing and Analysis is a comprehensive compilation of concepts and techniques used for processing and analyzing medical images after they have been generated or digitized. The Handbook is organized into six sections that relate to the main functions: enhancement, segmentation, quantification, registration, visualization, and compression, storage and communication.The second edition is extensively revised and updated throughout, reflecting new technology and research, and includes new chapters on: higher order statistics for tissue segmentation; tumor growth modeling in oncological image analysis; analysis of cell nuclear features in fluorescence microscopy images; imaging and communication in medical and public health informatics; and dynamic mammogram retrieval from web-based image libraries.For those looking to explore advanced concepts and access essential information, this second edition of Handbook of Medical Image Processing and Analysis is an invaluable resource. It remains the most complete single volume reference for biomedical engineers, researchers, professionals and those working in medical imaging and medical image processing.Dr. Isaac N. Bankman is the supervisor of a group that specializes on imaging, laser and sensor systems, modeling, algorithms and testing at the Johns Hopkins University Applied Physics Laboratory. He received his BSc degree in Electrical Engineering from Bogazici University, Turkey, in 1977, the MSc degree in Electronics from University of Wales, Britain, in 1979, and a PhD in Biomedical Engineering from the Israel Institute of Technology, Israel, in 1985. He is a member of SPIE. Includes contributions from internationally renowned authors from leading institutions NEW! 35 of 56 chapters have been revised and updated. Additionally, five new chapters have been added on important topics incluling Nonlinear 3D Boundary Detection, Adaptive Algorithms for Cancer Cytological Diagnosis, Dynamic Mammogram Retrieval from Web-Based Image Libraries, Imaging and Communication in Health Informatics and Tumor Growth Modeling in Oncological Image Analysis. Provides a complete collection of algorithms in computer processing of medical images Contains over 60 pages of stunning, four-color images