13,219 research outputs found
A finite element method applied to new active contour models and 3D reconstruction from cross sections
The use of energy-minimizing curves, known as "snakes" to extract features of interest in images has been introduced by Kass, Witkin & Terzopoulos. We present a model of deformation which solves some of the problems encountered with the original method such as instability and initial data while reducing the computational complexity. This model makes the curve behave like a balloon which is inflated by an additional force. The initial curve need no longer be close to the solution to converge. The external forces, that push the curve to the edges, are modified to give more stable results. The system is solved using a conform finite element method in the minimization process. The evolution to the equilibrium presents less oscillations, convergence obtained faster and the final results are more accurate. We have applied this model for segmenting ultrasound and magnetic resonance images. We have also made a first stage to 3D object reconstruction, by tracking the extracted contour on a series of successive cross sections
Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes
In this paper, we introduce a novel parametric method for segmentation of
three-dimensional images. We consider a piecewise constant version of the
Mumford-Shah and the Chan-Vese functionals and perform a region-based
segmentation of 3D image data. An evolution law is derived from energy
minimization problems which push the surfaces to the boundaries of 3D objects
in the image. We propose a parametric scheme which describes the evolution of
parametric surfaces. An efficient finite element scheme is proposed for a
numerical approximation of the evolution equations. Since standard parametric
methods cannot handle topology changes automatically, an efficient method is
presented to detect, identify and perform changes in the topology of the
surfaces. One main focus of this paper are the algorithmic details to handle
topology changes like splitting and merging of surfaces and change of the genus
of a surface. Different artificial images are studied to demonstrate the
ability to detect the different types of topology changes. Finally, the
parametric method is applied to segmentation of medical 3D images
Constrained Statistical Modelling of Knee Flexion from Multi-Pose Magnetic Resonance Imaging
Β© 1982-2012 IEEE.Reconstruction of the anterior cruciate ligament (ACL) through arthroscopy is one of the most common procedures in orthopaedics. It requires accurate alignment and drilling of the tibial and femoral tunnels through which the ligament graft is attached. Although commercial computer-Assisted navigation systems exist to guide the placement of these tunnels, most of them are limited to a fixed pose without due consideration of dynamic factors involved in different knee flexion angles. This paper presents a new model for intraoperative guidance of arthroscopic ACL reconstruction with reduced error particularly in the ligament attachment area. The method uses 3D preoperative data at different flexion angles to build a subject-specific statistical model of knee pose. To circumvent the problem of limited training samples and ensure physically meaningful pose instantiation, homogeneous transformations between different poses and local-deformation finite element modelling are used to enlarge the training set. Subsequently, an anatomical geodesic flexion analysis is performed to extract the subject-specific flexion characteristics. The advantages of the method were also tested by detailed comparison to standard Principal Component Analysis (PCA), nonlinear PCA without training set enlargement, and other state-of-The-Art articulated joint modelling methods. The method yielded sub-millimetre accuracy, demonstrating its potential clinical value
Mechanistic and pathological study of the genesis, growth, and rupture of abdominal aortic aneurysms
Postprint (published version
Conformational Dynamics of Supramolecular Protein Assemblies in the EMDB
The Electron Microscopy Data Bank (EMDB) is a rapidly growing repository for
the dissemination of structural data from single-particle reconstructions of
supramolecular protein assemblies including motors, chaperones, cytoskeletal
assemblies, and viral capsids. While the static structure of these assemblies
provides essential insight into their biological function, their conformational
dynamics and mechanics provide additional important information regarding the
mechanism of their biological function. Here, we present an unsupervised
computational framework to analyze and store for public access the
conformational dynamics of supramolecular protein assemblies deposited in the
EMDB. Conformational dynamics are analyzed using normal mode analysis in the
finite element framework, which is used to compute equilibrium thermal
fluctuations, cross-correlations in molecular motions, and strain energy
distributions for 452 of the 681 entries stored in the EMDB at present. Results
for the viral capsid of hepatitis B, ribosome-bound termination factor RF2, and
GroEL are presented in detail and validated with all-atom based models. The
conformational dynamics of protein assemblies in the EMDB may be useful in the
interpretation of their biological function, as well as in the classification
and refinement of EM-based structures.Comment: Associated online data bank available at:
http://lcbb.mit.edu/~em-nmdb
Three-Dimensional Motion Reconstruction and Analysis of the Right Ventricle Using Tagged MRI
Right ventricular (RV) dysfunction can serve as an indicator of heart and lung disease and can adversely affect the left ventricle (LV). However, normal RV function must be characterized before abnormal states can be detected. We can describe a method for reconstructing the 3D motion of the RV images by fitting of a deformable model to extracted tag and contour data from multiview tagged magnetic resonance images(MRI). The deformable model is a biventricular finite element mesh built directly from the contours. Our approach accommodates the geometrically complex RV by using the entire lengths of the tags, localized degrees of freedom (DOFs), and finite elements for geometric modeling. We convert the results of the reconstruction into potentially useful motion variables, such as strains and displacements. The fitting technique is applied to synthetic data, two normal hearts, and a heart with right ventricular hypertrophy (RVH). The results in this paper are limited to the RV free wall and septum. We find noticeable differences between the motion variables calculated for the normal volunteers and the RVH patient
Aquatics reconstruction software: the design of a diagnostic tool based on computer vision algorithms
Computer vision methods can be applied to a variety of medical and surgical applications, and many techniques and algorithms are available that can be used to recover 3D shapes and information from images range and volume data. Complex practical applications, however, are rarely approachable with a single technique, and require detailed analysis on how they can be subdivided in subtasks that are computationally treatable and that, at the same time, allow for the appropriate level of user-interaction. In this paper we show an example of a complex application where, following criteria of efficiency, reliability and user friendliness, several computer vision techniques have been selected and customized to build a system able to support diagnosis and endovascular treatment of Abdominal Aortic Aneurysms. The system reconstructs the geometrical representation of four different structures related to the aorta (vessel lumen, thrombus, calcifications and skeleton) from CT angiography data. In this way it supports the three dimensional measurements required for a careful geometrical evaluation of the vessel, that is fundamental to decide if the treatment is necessary and to perform, in this case, its planning. The system has been realized within the European trial AQUATICS (IST-1999-20226 EUTIST-M WP 12), and it has been widely tested on clinical data
Parametric Level Set Methods for Inverse Problems
In this paper, a parametric level set method for reconstruction of obstacles
in general inverse problems is considered. General evolution equations for the
reconstruction of unknown obstacles are derived in terms of the underlying
level set parameters. We show that using the appropriate form of parameterizing
the level set function results a significantly lower dimensional problem, which
bypasses many difficulties with traditional level set methods, such as
regularization, re-initialization and use of signed distance function.
Moreover, we show that from a computational point of view, low order
representation of the problem paves the path for easier use of Newton and
quasi-Newton methods. Specifically for the purposes of this paper, we
parameterize the level set function in terms of adaptive compactly supported
radial basis functions, which used in the proposed manner provides flexibility
in presenting a larger class of shapes with fewer terms. Also they provide a
"narrow-banding" advantage which can further reduce the number of active
unknowns at each step of the evolution. The performance of the proposed
approach is examined in three examples of inverse problems, i.e., electrical
resistance tomography, X-ray computed tomography and diffuse optical
tomography
A shape analysis approach to prediction of bone stiffness using FEXI
The preferred method of assessing the risk of an osteoporosis related fracture is currently a measure of bone mineral density (BMD) by dual energy X-ray absorptiometry (DXA). However, other factors contribute to the overall risk of fracture, including anatomical geometry and the spatial distribution of bone. Finite element analysis can be performed in both two and three dimensions, and predicts the deformation or induced stress when a load is applied to a structure (such as a bone) of defined material composition and shape. The simulation of a mechanical compression test provides a measure of whole bone stiffness (N mmβ1).
A simulation system was developed to study the sensitivity of BMD, 3D and 2D finite element analysis to variations in geometric parameters of a virtual proximal femur model. This study demonstrated that 3D FE and 2D FE (FEXI) were significantly more sensitive to the anatomical shape and composition of the proximal femur than conventional BMD. The simulation approach helped to analyse and understand how variations in geometric parameters affect the stiffness and hence strength of a bone susceptible to osteoporotic fracture.
Originally, the FEXI technique modelled the femur as a thin plate model of an assumed constant depth for finite element analysis (FEA). A better prediction of tissue depth across the bone, based on its geometry, was required to provide a more accurate model for FEA. A shape template was developed for the proximal femur to provide this information for the 3D FE analysis.
Geometric morphometric techniques were used to procure and analyse shape information from a set of CT scans of excised human femora. Generalized Procrustes Analysis and Thin Plate Splines were employed to analyse the data and generate a shape template for the proximal femur. 2D Offset and Depth maps generated from the training set data were then combined to model the three-dimensional shape of the bone. The template was used to predict the three-dimensional bone shape from a 2D image of the proximal femur procured through a DXA scan. The error in the predicted 3D shape was measured as the difference in predicted and actual depths at each pixel. The mean error in predicted depths was found to be 1.7mm compared to an average bone depth of 34mm.
3D FEXI analysis on the predicted 3D bone along with 2D FEXI for a stance loading condition and BMD measurement were performed based on 2D radiographic projections of the CT scans and compared to bone stiffness results obtained from finite element analysis of the original 3D CT scans. 3D FEXI provided a significantly higher correlation (R2 = 0.85) with conventional CT derived 3D finite element analysis than achieved with both BMD (R2 = 0.52) and 2D FEXI (R2 = 0.44)
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