Thiranja P. Babarenda Gamage

OpenCMISS: A multi-physics & multi-scale computational infrastructure for the VPH/Physiome project

The VPH/Physiome Project is developing the model encoding standards CellML (cellml.org) and FieldML (fieldml.org) as well as web-accessible model repositories based on these standards (models.physiome.org). Freely available open source computational modelling software is also being developed to solve the partial differential equations described by the models and to visualise results. The OpenCMISS code (opencmiss.org), described here, has been developed by the authors over the last six years to replace the CMISS code that has supported a number of organ system Physiome projects. OpenCMISS is designed to encompass multiple sets of physical equations and to link subcellular and tissue-level biophysical processes into organ-level processes. In the Heart Physiome project, for example, the large deformation mechanics of the myocardial wall need to be coupled to both ventricular flow and embedded coronary flow, and the reaction-diffusion equations that govern the propagation of electrical waves through myocardial tissue need to be coupled with equations that describe the ion channel currents that flow through the cardiac cell membranes. In this paper we discuss the design principles and distributed memory architecture behind the OpenCMISS code. We also discuss the design of the interfaces that link the sets of physical equations across common boundaries (such as fluid-structure coupling), or between spatial fields over the same domain (such as coupled electromechanics), and the concepts behind CellML and FieldML that are embodied in the OpenCMISS data structures. We show how all of these provide a flexible infrastructure for combining models developed across the VPH/Physiome community.

Breast lesion co-localisation between X-ray and MR images using finite element modelling

This paper presents a novel X-ray and MR image registration technique based on individual-specific biomechanical finite element (FE) models of the breasts. Information from 3D magnetic resonance (MR) images was registered to X-ray mammographic images using non-linear FE models subject to contact mechanics constraints to simulate the large compressive deformations between the two imaging modalities. A physics-based perspective ray-casting algorithm was used to generate 2D pseudo-X-ray projections of the FE-warped 3D MR images. Unknown input parameters to the FE models, such as the location and orientation of the compression plates, were optimised to provide the best match between the pseudo and clinical X-ray images. The methods were validated using images taken before and during compression of a breast-shaped phantom, for which 12 inclusions were tracked between imaging modalities. These methods were then applied to X-ray and MR images from six breast cancer patients. Error measures (such as centroid and surface distances) of segmented tumours in simulated and actual X-ray mammograms were used to assess the accuracy of the methods. Sensitivity analysis of the lesion co-localisation accuracy to rotation about the anterior-posterior axis was then performed. For 10 of the 12 X-ray mammograms, lesion localisation accuracies of 14 mm and less were achieved. This analysis on the rotation about the anterior-posterior axis indicated that, in cases where the lesion lies in the plane parallel to the mammographic compression plates, that cuts through the nipple, such rotations have relatively minor effects.This has important implications for clinical applicability of this multi-modality lesion registration technique, which will aid in the diagnosis and treatment of breast cancer.

Modelling Prone to Supine Breast Deformation Under Gravity Loading Using Heterogeneous Finite Element Models

Biomechanical models of the breast can be used to co-locate information between the various medical images to identify tumour locations, while also providing the ability to predict their locations during surgical procedures. We have created subject-specific, heterogeneous 3D finite element (FE) models of breast biomechanics to provide the ability to predict breast deformation under different loading conditions. We have verified the applicability of such modelling for simulating the prone to supine reorientation of the breast and obtained good agreement to ground-truth supine images obtained from breast MRI. In particular, we highlight the importance of modelling the pectoral muscles for gravity loading simulations.

Surface deformation tracking of a silicone gel skin phantom in response to normal indentation

Identifying the mechanical properties of the skin has been the subject of much study in recent years, as such knowledge can provide insight into wound healing, wrinkling and minimization of scarring through surgical planning.

Patient-Specific Modeling of Breast Biomechanics with Applications to Breast Cancer Detection and Treatment

There are many challenges clinicians are faced with when diagnosing and treating breast cancer. Biomechanical modeling of the breast is a field of research that aims to assist clinicians by providing a physics based approach to addressing some of these challenges. This review describes the state of the art in the field, from aiding co-location of information between various medical imaging modalities used to identify tumours; to providing the ability to predict the location of these tumors during different biopsy or surgical procedures; to aiding temporal registration of follow-up medical images used to review the progress of suspicious lesions and therefore evaluate effectiveness of breast cancer treatments; to aiding implant selection for breast augmentation procedures and the subsequent prediction of the resulting appearance following such procedures. Significant technical challenges remain in terms of improving the accuracy of such biomechanical models. These include the precise determination and application of loading and boundary constraints applied during different clinical procedures, and accurate characterization of individual-specific mechanical properties of the different breast tissues. In addition to these more technical challenges, a number of practical challenges exist when translating biomechanical models from research based environments into clinical workflows, which demand general applicability, and ease and speed of use. This review outlines such challenges and provides an overview of the steps researchers are taking to address them. Once these challenges have been met, there is potential for extending the use of biomechanics to simulate more complex clinical procedures, from modeling needle insertions into breast tissue during real-time biopsy procedures, to simulating and predicting the outcome of different surgical procedures such as tumorectomies. Clinical adoption of such state-of-the-art modeling techniques has significant potential for reducing the number of misdiagnosed breast cancers while also helping improve clinical treatment of patients.

Clinical Applications of Breast Biomechanics

Clinicians face many challenges when diagnosing and treating breast cancer. Biomechanical modeling of the breast is a field of research that aims to assist clinicians in addressing some of these challenges using a physics-based approach. This includes helping to colocate information between various medical imaging modalities that are used to identify tumors, and providing the ability to predict the location of these tumors during different treatment procedures. This review describes the state of the art in the field of modeling breast biomechanics, highlighting methods that have been used for constructing biomechanical models of the breast, and the constitutive relations that are typically used to describe the behavior of its different tissues. Examples of how these models have been used to address clinical challenges are also presented. Clinical adoption of such modeling techniques has significant potential for reducing instances of misdiagnosed breast cancers, while also helping to improve clinical treatment of patients.

Relationship Between Structure and Mechanics for Membranous Tissues

Professor Yoram Lanir has pioneered the development of structurally based constitutive relations to describe the stressstrain response of soft biological tissues. This approach relates the mechanical response of the tissue to the intrinsic micro-structural properties of its constituents, such as collagen. This article summarises some of the work by the Auckland Bioengineering Institute contributing towards the goal of understanding the structure–function relationship of soft membranous tissue. Key aspects of our work are to (1) develop constitutive relations based on quantitative information of tissue structure; and (2) use rich sets of experimental data to aid in accurate and reliable constitutive parameter identification. We first outline several common techniques to quantify tissue structure, such as collagen fibre orientations. A detailed description of an extended-volume imaging system, developed in our laboratory, is then provided along with a few application examples. The gathered imaging data is incorporated into structural constitutive models by means of fitting to mathematical distributions. Based upon the observations made from some imaging studies, a conceptual fibre distribution model is proposed for modelling the collagen network in skin. We then introduce a selection of constitutive models, which have been developed to characterise the mechanical behaviour of soft connective tissues (skin in particular), with particular emphasis on structurally based models. Finite element models, used with appropriate constitutive relations, provide a means of interpreting experimental results. Some of our recent efforts in developing instrumentation to measure the two-dimensional and three-dimensional response of soft tissues are described. This includes a biaxial tensile rig, which is capable of deforming membranes in up to 16 directions, and a force-sensitive micro-robot. We highlight some of the challenges often associated with constitutive parameter identification using commonly used model based fitting approaches. These issues were examined and illustrated in depth by performing controlled studies on silicon gel phantoms, which allowed us to focus our attention solely on the identification problem. Lastly, future directions of applying structurally based models to understanding the biomechanics of soft tissues are discussed.

Surface deformation tracking and modeling of soft materials

Characterizing the mechanical properties of skin may lead to improvements in surgical scarring, burns treatments, artificial skin science, and disease detection. We present a method of validating a phase-based crosscorrelation method of material point tracking, used to measure surface deformations in soft tissues, using a silicone gel phantom. Tracking of a high spatial-resolution speckle pattern was validated using independent fluorescent microsphere markers. A finite element mesh was deformed according to the tracked speckle pattern, and used to predict the location of the markers. Predictions of microsphere location were compared to stereo-reconstructions. Under a 2900 μm indentation, markers under rms displacements of 125 μm produced a discrepancy between prediction and reconstruction of 23 μm. The same deformation conditions were used to illustrate the use of surface tracking for identifying mechanical properties. A force-driven finite element mesh, using a Neo-Hookean constitutive model, reproduced the surface deformation with an rms error of 172 μm.

3D surface profiling using arbitrarily positioned cameras

3D surface measurements are important for studying the biomechanical properties of deformable tissues. For 3D surface profiling and reconstruction, corresponding points on an object should be matched in different camera views. This process is traditionally performed in systems that use stereo camera pairs or multiple cameras with aligned optical axes. To measure the deformation in soft tissues, it may be more appropriate to arbitrarily position the cameras. For instance, cameras can be placed to overcome obstructions that may be caused by measurement apparatus, such as a surface indenter. A truly arbitrary placement system requires the development of a new algorithm for finding corresponding points during surface reconstruction, as existing methods cannot handle large incompatibilities due to the perspective effects between rotated camera views. In this study, we have proposed a procedure for feature matching that can be used with arbitrarily positioned cameras. This proposed method is then used to generate a 3D surface profile of a silicone gel phantom.

Towards a Real-Time Full-Field Stereoscopic Imaging System for Tracking Lung Surface Deformation Under Pressure Controlled Ventilation

The normal decline in lung function that occurs with age is virtually indistinguishable from early disease, leading to frequent misdiagnosis in the elderly. Computational modelling promises to be a useful tool for improving our understanding of lung mechanics. However, there is currently no unified structure-function computational model that explains how age-dependent structural changes translate to decline in whole lung function. Furthermore, existing models suffer from weak parameterisation due to lack of available data. To begin addressing this issue, we have developed a real-time full-field stereoscopic imaging system for tracking surface deformation of the rat lung during pressure-controlled ventilation. The system will enable the acquisition of novel physiological data on lung tissue mechanics. This study presents preliminary lung surface tracking results from experiments on Sprague-Dawley rats under pressure controlled ventilation. This rich data will provide us with previously unavailable information for constructing and validating more realistic computational models of the lung to help us better understand the mechanisms behind decline in lung function with aging and help guide the development of new diagnostic methods to distinguish age from lung disease.