Heart illnesses influence the functioning of the cardiac muscle and are the major causes of death in
the world. Optic flow methods are essential tools to assess and quantify the contraction of the cardiac
walls, but are hampered by the aperture problem. Harmonic phase (HARP) techniques measure the
phase in magnetic resonance (MR) tagged images. Due to the regular geometry, patterns generated by
a combination of HARPs and sine HARPs represent a suitable framework to extract landmark features.
In this paper we introduce a new aperture-problem free method to study the cardiac motion by tracking
multi-scale features such as maxima, minima, saddles and corners, on HARP and sine HARP tagged
images.

Cardiac motion estimation using multi-scale feature points

The assignment of bone material properties to finite element model is a fundamental step in finite element analysis and has great influence on analysis results. Most work done in this area has adopted isotropic assignment strategy as its simplicity. However, bone material is widely recognized as being anisotropic rather than isotropic. Therefore, this work is aimed to simulate the inhomogeneity and anisotropy of femur by assigning each element of the mesh model the material properties with a numerical integration method and properly defining the principal material orientation, and then compare the biomechanical analysis results of isotropic model with that of anisotropic model under six different loading conditions. Based on the analysis results of the equivalent Von Mises stress and the nodal displacement, four different regions of interest are chosen to achieve this comparison. The results show that significant differences between the two material property assignments exist in the regions where anisotropic material property is sensitive to orientation definition. Thus, orientation definition is important to finite element simulation of bone material properties.

Orientation definition of anisotropy is important to finite element simulation of bone material properties

We present a framework for cardiac motion recovery using the adjustment of an electromechanical model of the heart to cine Magnetic Resonance Images (MRI). This approach is based on a constrained minimisation of an energy coupling the model and the data. Our method can be seen as a data assimilation of a dynamic system that allows us to weight appropriately the confidence in the model and the confidence in the data. After a short overview of the electromechanical model of the ventricles, we describe the processing of cine MR images and the methodology for motion recovery. Then, we compare this method to the methodology used in data assimilation. Presented results on motion recovery from given cine-MRI are very promising. In particular, we show that our coupling approach allows us to recover some tangential component of the ventricles motion which cannot be obtained from classical geometrical tracking approaches due to the aperture problem.

Cardiac Motion Recovery by Coupling an Electromechanical Model and Cine-MRI Data: First Steps

Surgical repair of the mitral valve results in better
outcomes than valve replacement, yet diseased valves
are often replaced due to the technical difficulty of the
repair process. A surgical planning system based on
patient-specific medical images that allows surgeons to
simulate and compare potential repair strategies could
greatly improve surgical outcomes. The system must
simulate valve closure quickly and handle the complex
boundary conditions imposed by the chords that tether
the valve leaflets. We have developed a process for
generating a triangulated mesh of the valve surface
from volumetric image data of the opened valve. The
closed position of the mesh is then computed using a
mass-spring model of dynamics. In the mass-spring
model, triangle sides are treated as linear springs
supporting only tension. Chords are also treated as
linear springs, and self-collisions are detected and
handled inelastically. The equations of motion are
solved using implicit numerical integration. The
simulated closed state is compared with an image of the
same valve taken in the closed state to assess accuracy
of the model. The model exhibits rapid valve closure
and is able to predict the closed state of the valve with
reasonable accuracy.

Fast image-based model of mitral valve closure for surgical planning

The elastic registration of medical scans from different acquisition sequences is becoming an important topic for many research labs that would like to continue the post-processing of medical scans acquired via the new generation of high-field-strength scanners. In this note, we present a parameter-free registration algorithm that is well suited for this scenario as it requires no tuning to specific acquisition sequences. The algorithm encompasses a new numerical scheme for computing elastic registration maps based on the minimizing flow approach to optimal mass transport. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A. Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. We apply the algorithm to register the white matter folds of two different scans and use the results to parcellate the cortex of the target image. To the best of our knowledge, this is the first time that the optimal mass transport function has been applied to register large 3D multimodal data sets.

Multimodal Registration of White Matter Brain Data via Optimal Mass Transport

Very little is known about the deformation effects of tumour growth within the brain. Computer simulations have the potential to
calculate such deformations. A method for computing localised high deformations within the brain's soft tissue is presented. Such
knowledge would be significant towards neuroscience and neurosurgery, particularly for quantifying tumour aggressiveness,
therapy planning, as well as surgical planning and simulation. A Finite Element mesh used in the vicinity of a growing tumour is
very quickly destroyed and cannot be used reliably unless complicated automatic re-meshing exists. Mesh-free methods are
capable of handling much larger deformations, however are known to be less reliable that Finite Element analysis for moderate
deformations. A mixed-mesh approach utilises mesh-free regions within localised high-deformation zones, with the remaining
model comprised of a Finite Element mesh. In this study, a new algorithm is proposed coupling the Finite Element and Element
Free Galerkin methods for use in applications of high localised deformation, such as brain tumour growth. The algorithm is
verified against a number of separate Finite Element and mesh-free problems solved via validated/commercial software.
Maximum errors of less than 0.85 mm were maintained, corresponding to the working resolution of an MRI scan. A mixed-mesh
brain model is analysed with respect to different tumour growth volumes located behind the left ventricle. Significant
displacements of up to 9.66 mm surrounding a 4118 mm3 sized tumour are noted, with 14.5% of the brain mesh suffering
deformation greater than 5 mm.

Coupling Finite Element and Mesh-free Methods for Modelling Brain Deformation in Response to Tumour Growth

A large number of pathological conditions result in significant changes of the mechanical properties of the aortic wall. Using the Pulse Wave Velocity (PWV) as an indicator of aortic stiffness has been proposed for several decades. Pulse Wave Imaging (PWI) is an ultrasonography-based imaging method that has been developed to map and quantify the pulse wave (PW) propagation along the abdominal aortic wall and measure its local properties. We present a finite-element-based approach that aims at improving our understanding of the complex PW patterns observed by PWI and their relationship to the underlying mechanical properties. A Fluid-Structure Interaction (FSI) coupled model was developed based on an idealized axisymmetric aorta geometry. The accuracy of the model as well as its ability to reproduce realistic PW propagation were evaluated by performing a parametric analysis on aortic elasticity, by varying the aortic Young�s modulus between 20 kPa and 2000 kPa. The Finite-Element model was able to predict with good accuracy the expected PWV values in different theoretical cases, with an averaged relative difference of 14% in the 20kPa-100kPa, which corresponds to a wide physiologic range for stiffness of the healthy aorta. This study allows to validate the proposed FE model as a tool that is capable of representing quantitatively the pulse wave patterns in the aorta.

Finite Element Modeling of the Pulse Wave propagation in the aorta for simulation of the Pulse Wave Imaging (PWI) method

This paper introduces an integrated 3D medical image
processing and analyzing software platform which is open interface
and freely available. The platform consists of the Medical Imaging
Toolkit (MITK) and the 3-Dimensional Medical Image Processing and
Analyzing System (3DMed). MITK is an algorithm toolkit for research
and software development, while 3DMed is a MITK based application
system with a plug-in framework. The overall architecture and main
capabilities of the platform are described in detail. Presented
evaluations demonstrate that the platform can benefit the
development of computer assisted intervention systems.

MITK and 3DMed : An Integrated Platform Applicable for the Development of Computer Assisted Intervention Systems

In this paper the Coronary Artery Tracking competition, which was part of the workshop: "3D Segmentation in the Clinic: A Grand Challenge II" is described. This workshopwas held during the 2008 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference. An introduction is given to underline the importance of (semi-)automatic coronary artery centerline extraction methods
and the advantages of an online framework facilitating a fair comparison of these methods. Furthermore, information is provided about the set-up of the workshop, the evaluation measures used and the online framework. Results for the algorithms, submitted by both industrial and academic research institutes, are presented as well.

3D Segmentation in the Clinic: A Grand Challenge II - Coronary Artery Tracking

This coronary-artery extraction method uses one initialization point per vessel. First, a mask is computed by use of a region-growing algorithm, which starts from the initial point and stops when no more connected voxels fall within an interactively defined intensity range. The centerline tracking is then
performed within the mask, starting from the same initial point. This algorithm is based on a prediction/estimation scheme. It uses the first- and second-order image moments calculated within a spherical volume that slides along the vessel, and the radius of which is automatically adjusted to the local radius
of the vessel. The evolution of the radius of the sphere is based on the analysis of the eigenvalues of the inertia matrix in a multi-scale framework. The estimation of the current point location makes use
of an elastic model similar to ”snakes”. The point iteratively moves under the action of an image-force attracting it to the local gravity center, and under the reaction of the internal forces of the model, which
reflect its shape constraints: continuity and smoothness. The prediction makes use of the eigenvectors of
the inertia matrix. The stopping criteria of the centerline tracking are based on the size of the sphere and on the percentage of the masked voxels within the sphere.
On 8 training CT datasets, the following mean results were obtained. Overlap with reference: considering the whole length (OV) 80.1%, until the first failure (OF) 48.9%, in clinically relevant segments (radius > 1.5 mm, OT) 81.7%. Average distance from reference: considering the whole length
(AD) 4.32 mm, limited to segments where the semiautomatic centerline remains within the vessel (AI) 0.39 mm, in clinically relevant segments (AT) 4.13 mm. On 16 testing datasets, these results were respectively: OV =80.2%, OF =39.3%, OT =82.1%, AD =5.05 mm, AI =0.41 mm and AT =4.58 mm. 
A number of failures was due to the the fact that the model does not handle the bifurcations.