Patient-specific anisotropic model of human trunk based on MR data

There are many ways to generate geometrical models for numerical simulation, and most of them start with a segmentation step to extract the boundaries of the regions of interest. This paper presents an algorithm to generate a patient-specific three-dimensional geometric model, based on a tetrahedral mesh, without an initial extraction of contours from the volumetric data. Using the information directly available in the data, such as gray levels, we built a metric to drive a mesh adaptation process. The metric is used to specify the size and orientation of the tetrahedral elements everywhere in the mesh. Our method, which produces anisotropic meshes, gives good results with synthetic and real MRI data. The resulting model quality has been evaluated qualitatively and quantitatively by comparing it with an analytical solution and with a segmentation made by an expert. Results show that our method gives, in 90% of the cases, as good or better meshes as a similar isotropic method, based on the accuracy of the volume reconstruction for a given mesh size. Moreover, a comparison of the Hausdorff distances between adapted meshes of both methods and ground-truth volumes shows that our method decreases reconstruction errors faster. Copyright © 2015 John Wiley & Sons, Ltd.Natural Sciences and Engineering Research Council (NSERC) of Canada and the MEDITIS training program (´Ecole Polytechnique de Montreal and NSERC)

Anisotropic Mesh Adaptation for Image Representation

Triangular meshes have gained much interest in image representation and have been widely used in image processing. This paper introduces a framework of anisotropic mesh adaptation (AMA) methods to image representation and proposes a GPRAMA method that is based on AMA and greedy-point removal (GPR) scheme. Different than many other methods that triangulate sample points to form the mesh, the AMA methods start directly with a triangular mesh and then adapt the mesh based on a user-defined metric tensor to represent the image. The AMA methods have clear mathematical framework and provides flexibility for both image representation and image reconstruction. A mesh patching technique is developed for the implementation of the GPRAMA method, which leads to an improved version of the popular GPRFS-ED method. The GPRAMA method can achieve better quality than the GPRFS-ED method but with lower computational cost.Comment: 25 pages, 15 figure

3D MODELLING AND RAPID PROTOTYPING FOR CARDIOVASCULAR SURGICAL PLANNING – TWO CASE STUDIES

In the last years, cardiovascular diagnosis, surgical planning and intervention have taken advantages from 3D modelling and rapid prototyping techniques. The starting data for the whole process is represented by medical imagery, in particular, but not exclusively, computed tomography (CT) or multi-slice CT (MCT) and magnetic resonance imaging (MRI). On the medical imagery, regions of interest, i.e. heart chambers, valves, aorta, coronary vessels, etc., are segmented and converted into 3D models, which can be finally converted in physical replicas through 3D printing procedure. In this work, an overview on modern approaches for automatic and semiautomatic segmentation of medical imagery for 3D surface model generation is provided. The issue of accuracy check of surface models is also addressed, together with the critical aspects of converting digital models into physical replicas through 3D printing techniques. A patient-specific 3D modelling and printing procedure (Figure 1), for surgical planning in case of complex heart diseases was developed. The procedure was applied to two case studies, for which MCT scans of the chest are available. In the article, a detailed description on the implemented patient-specific modelling procedure is provided, along with a general discussion on the potentiality and future developments of personalized 3D modelling and printing for surgical planning and surgeons practice

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

Automatic Mesh-Based Segmentation of Multiple Organs in MR Images

La segmentation de structures anatomiques multiples dans des images de résonance magnétique (RM) est souvent requise dans des applications de génie biomédical telles que la simulation numérique, la chirurgie guidée par l’image, la planification de traitements, etc. De plus, il y a un besoin croissant pour une segmentation automatique d’organes multiples et de structures complexes à partir de cette modalité d’imagerie. Il existe plusieurs techniques de segmentation multi-objets qui ont été appliquées avec succès sur des images de tomographie axiale à rayons-X (CT). Cependant, dans le cas des images RM cette tâche est plus difficile en raison de l’inhomogénéité des intensités dans ces images et de la variabilité dans l’apparence des structures anatomiques. Par conséquent, l’état de l’art sur la segmentation multi-objets sur des images RM est beaucoup plus faible que celui sur les images CT. Parmi les travaux qui portent sur la segmentation d’images RM, les approches basées sur la segmentation de régions sont sensibles au bruit et la non uniformité de l’intensité dans les images. Les approches basées sur les contours ont de la difficulté à regrouper les informations sur les contours de sorte à produire un contour fermé cohérent. Les techniques basées sur les atlas peuvent avoir des problèmes en présence de structures complexes avec une grande variabilité anatomique. Les modèles déformables représentent une des méthodes les plus populaire pour la détection automatique de différents organes dans les images RM. Cependant, ces modèles souffrent encore d’une limitation importante qui est leur sensibilité à la position initiale et la forme du modèle. Une initialisation inappropriée peut conduire à un échec dans l’extraction des frontières des objets. D’un autre côté, le but ultime d’une segmentation automatique multi-objets dans les images RM est de produire un modèle qui peut aider à extraire les caractéristiques structurelles d’organes distincts dans les images. Les méthodes d’initialisation automatique actuelles qui utilisent différents descripteurs ne réussissent pas complètement l’extraction d’objets multiples dans les images RM. Nous avons besoin d’exploiter une information plus riche qui se trouve dans les contours des organes. Dans ce contexte les maillages adaptatifs anisotropiques semblent être une solution potentielle au problème soulevé. Les maillages adaptatifs anisotropiques construits à partir des images RM contiennent de l’information à un plus haut niveau d’abstraction représentant les éléments, d’une orientation et d’une forme donnée, qui constituent les différents organes dans l’image. Les méthodes existantes pour la construction de maillages adaptatifs sont basées sur les intensités dans l’image et possèdent une limitation pratique qui est l’alignement inadéquat des éléments du maillage en présence de contours inclinés dans l’image. Par conséquent, nous avons aussi besoin d’améliorer le processus d’adaptation de maillage pour produire une meilleure représentation de l’image basée sur un maillage.----------ABSTRACT: Segmentation of multiple anatomical structures in MR images is often required for biomedical engineering applications such as clinical simulation, image-guided surgery, treatment planning, etc. Moreover, there is a growing need for automatic segmentation of multiple organs and complex structures from this medical imaging modality. Many successful multi-object segmentation attempts were introduced for CT images. However in the case of MR images it is a more challenging task due to intensity inhomogeneity and variability of anatomy appearance. Therefore, state-of-the-art in multi-object MR segmentation is very inferior to that of CT images. In literature dealing with MR image segmentation, the region-based approaches are sensitive to noise and non-uniformity in the input image. The edge-based approaches are challenging to group the edge information into a coherent closed contour. The atlas-based techniques can be problematic for complicated structures with anatomical variability. Deformable models are among the most popular methods for automatic detection of different organs in MR images. However they still have an important limitation which is that they are sensitive to initial position and shape of the model. An unsuitable initialization may provide failure to capture the true boundaries of the objects. On the other hand, a useful aim for an automatic multi-object MR segmentation is to provide a model which promotes understanding of the structural features of the distinct objects within the MR images. The current automatic initialization methods which have used different descriptors are not completely successful in extracting multiple objects from MR images and we need to find richer information that is available from edges. In this regard, anisotropic adaptive meshes seem to be a potential solution to the aforesaid limitation. Anisotropic adaptive meshes constructed from MR images contain higher level, abstract information about the anatomical structures of the organs within the image retained as the elements shape and orientation. Existing methods for constructing adaptive meshes based on image features have a practical limitation where manifest itself in inadequate mesh elements alignment to inclined edges in the image. Therefore, we also have to enhance mesh adaptation process to provide a better mesh-based representation. In this Ph.D. project, considering the highlighted limitations we are going to present a novel method for automatic segmentation of multiple organs in MR images by incorporating mesh adaptation techniques. In our progress, first, we improve an anisotropic adaptation process for the meshes that are constructed from MR images where the mesh elements align adequately to the image content and improve mesh anisotropy along edges in all directions. Then the resulting adaptive meshes are used for initialization of multiple active models which leads to extract initial object boundaries close to the true boundaries of multiple objects simultaneously. Finally, the Vector Field Convolution method is utilized to guide curve evolution towards the object boundaries to obtain the final segmentation results and present a better performance in terms of speed and accuracy

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

A physically based trunk soft tissue modeling for scoliosis surgery planning systems

One of the major concerns of scoliotic patients undergoing spinal correction surgery is the trunk's external appearance after the surgery. This paper presents a novel incremental approach for simulating postoperative trunk shape in scoliosis surgery. Preoperative and postoperative trunk shapes data were obtained using three-dimensional medical imaging techniques for seven patients with adolescent idiopathic scoliosis. Results of qualitative and quantitative evaluations, based on the comparison of the simulated and actual postoperative trunk surfaces, showed an adequate accuracy of the method. Our approach provides a candidate simulation tool to be used in a clinical environment for the surgery planning process.IRSC / CIH

Optimization of CT scanning protocol of Type B aortic dissection follow-up through 3D printed model

This research aims to develop and evaluate a human tissue-like material 3D printed model used as a phantom in determining optimized scanning parameters to reduce the radiation dose for Type B aortic dissection patients after thoracic endovascular aortic repair. The results show that radiation risk for follow-up Type B aortic dissection patients can be potentially reduced. Further, the value of using 3D printed model in studying CT scanning protocols was further validated

Development of Human Body CAD Models and Related Mesh Processing Algorithms with Applications in Bioelectromagnetics

Simulation of the electromagnetic response of the human body relies heavily upon efficient computational CAD models or phantoms. The Visible Human Project (VHP)-Female v. 3.1 - a new platform-independent full-body electromagnetic computational model is revealed. This is a part of a significant international initiative to develop powerful computational models representing the human body. This model’s unique feature is full compatibility both with MATLAB and specialized FEM computational software packages such as ANSYS HFSS/Maxwell 3D and CST MWS. Various mesh processing algorithms such as automatic intersection resolver, Boolean operation on meshes, etc. used for the development of the Visible Human Project (VHP)-Female are presented. The VHP - Female CAD Model is applied to two specific low frequency applications: Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS). TMS and tDCS are increasingly used as diagnostic and therapeutic tools for numerous neuropsychiatric disorders. The development of a CAD model based on an existing voxel model of a Japanese pregnant woman is also presented. TMS for treatment of depression is an appealing alternative to drugs which are teratogenic for pregnant women. This CAD model was used to study fetal wellbeing during induced peak currents by TMS in two possible scenarios: (i) pregnant woman as a patient; and (ii) pregnant woman as an operator. An insight into future work and potential areas of research such as a deformable phantom, implants, and RF applications will be presented

The Functional, Ecological, and Evolutionary Morphology of Sea Lampreys (Petromyzon marinus)

Lampreys (Petromyzontiformes) are jawless vertebrates with an evolutionary history lasting at least 360 million years and are often used in comparisons with jawed vertebrates because some of their morphological aspects, such as the segmented trunk musculature with curved myosepta and a non-mineralized skeleton fibrous skeleton, are thought to resemble the condition of early vertebrates before the evolution of jaws. Although earlier authors studied the morphology of the skeleto-muscular system of the trunk of lampreys, their studies are not detailed and complete enough to allow a functional and biomechanical analysis that is needed as a basis for modeling the mechanics of lamprey locomotion and for understanding the causal roles played by the anatomical structures within the trunk. Questions remain, such as what is the architecture of the trunk fibroskeleton, and how does it function with the musculature to bend the trunk? This dissertation studied the functional, ecological and evolutionary morphology of the trunk of Sea Lampreys (Petromyzon marinus) as well as its relevance in understanding the environmental history of landlocked lamprey populations. Functional morphology revealed that the fibroskeleton of the trunk is a self-supporting concatenated system of fibers, which creates a scaffold for the musculature and transmits forces to bend the trunk during swimming. Ecological morphology demonstrated the adaptive advantage of the fibroskeleton’s architecture, which enables the movements that are performed during migration and spawning and gives lampreys the capacity to colonize upstream realms. These results help explain the evolutionary morphology of lampreys, which likely originated in freshwater as algal feeders and evolved into parasites after going through an intermediary scavenging stage. When these insights are applied to the evolution of landlocked Sea Lampreys, it becomes evident that their entry into freshwater lakes occurred as soon as they were able to reach them and that populations likely became established in Lake Ontario, Lake Champlain, and the Finger Lakes thousands of years ago. This insight undermines the current status of landlocked Sea Lampreys as invasive species in these lakes and the case for their eradication. Hence, this dissertation provides a comprehensive and integrative analysis of lamprey biology from their anatomy to environmental policy