Primary stability of osseointegrated implants is necessary for short and long-term success of the treatment. This paper presents a method to help clinicians preoperatively assess this primary implant stability. The method combines a planning software with a in-house finite element solver. Once the clinician has chosen a position for the implant on the planning tool, a finite element analysis is automatically started and calculates the mechanical stability of the implant at this position. The process is designed to be as simple and fast as possible for an efficient clinical use. Mechanical testing material was used to validate the stability measured by the software. The novel tool presented here leads the way to a new generation of intelligent computer-assisted tools able to give a priori indication on the life span of the implant.

Predicting implant stability: in vitro validation in artificial bone

Meaningful physical models are important for studying cardiac physiology, such as quantitative assessments of pathology via changes in model parameters, and recovering information from medical images. In order to achieve realistic deformation studies, an anatomically accurate cardiac model under the prolate spheroidal coordinate system has been proposed, which comprises the pole-zero constitutive law characterized by 18 material parameters. Nevertheless, the large number of parameters and the complicated mathematics under the curvilinear coordinate system make it difficult to implement and computationally expensive. In consequence, we propose a cardiac model under the cartesian coordinate system comprising the Costa law, which is tailored for medical image analysis. The Costa law is characterized by a strain energy function with only seven material parameters, but has been reported as the best among the five tested well-known models in a comparative study, including the pole-zero law. In our framework, the penalty method for material incompressibility is used to avoid introduction of extra variables. Furthermore, we introduce a simple but novel boundary condition for enforcing cardiac specific boundary displacements under the cartesian coordinate system. With the active stresses provided by cardiac electromechanical models, and also the blood pressures acting as the natural boundary conditions on the endocardial surfaces, the physiologically plausible active deformation of the heart can be simulated. Experiments have been done on a cubical object to verify the correctness of the implementation, and also on a canine heart architecture to show the physiological plausibility of the cardiac model.

Simulation of Active Cardiac Dynamics with Orthotropic Hyperelastic Material Model

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.