ORCID Profile
0000-0002-6466-4936
Current Organisation
The University of Auckland
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Biomechanics | Biomechanical Engineering | Biomedical Engineering
Expanding Knowledge in Engineering | Expanding Knowledge in the Biological Sciences |
Publisher: SPIE
Date: 06-03-2008
DOI: 10.1117/12.769945
Publisher: SPIE
Date: 14-04-2005
DOI: 10.1117/12.596178
Publisher: Wiley
Date: 16-02-2011
DOI: 10.1002/CNM.1429
Publisher: Elsevier BV
Date: 11-2008
DOI: 10.1016/J.ACRA.2008.07.017
Abstract: Anatomically realistic biomechanical models of the breast potentially provide a reliable way of mapping tissue locations across medical images, such as mammograms, magnetic resonance imaging (MRI), and ultrasound. This work presents a new modeling framework that enables us to create biomechanical models of the breast that are customized to the in idual. We demonstrate the framework's capabilities by creating models of the left breasts of two volunteers and tracking their deformations across MRIs. We generate customized finite element models by automatically fitting geometrical models to segmented data from breast MRIs, and characterizing the in vivo mechanical properties (assuming homogeneity) of the breast tissues. For each volunteer, we identified the unloaded configuration by acquiring MRIs of the breast under neutral buoyancy (immersed in water). Such imaging is clearly not practical in the clinical setting however, these previously unavailable data provide us with important data with which to validate models of breast biomechanics. Internal tissue features were identified in the neutral buoyancy images and tracked to the prone gravity-loaded state using the modeling framework. The models predicted deformations with root-mean-square errors of 4.2 and 3.6 mm in predicting the skin surface of the gravity-loaded state for each volunteer. Internal tissue features were tracked with a mean error of 3.7 and 4.7 mm for each volunteer. The models capture breast shape and internal deformations across the images with clinically acceptable accuracy. Further refinement of the framework and incorporation of more anatomic detail will make these models useful for breast cancer diagnosis.
Publisher: Elsevier BV
Date: 10-2011
DOI: 10.1016/J.PBIOMOLBIO.2011.06.015
Abstract: 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 ex le, 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.
Publisher: SPIE
Date: 26-02-2009
DOI: 10.1117/12.811789
Publisher: Springer Science and Business Media LLC
Date: 09-01-2008
DOI: 10.1007/S10237-006-0074-6
Abstract: A number of biomechanical models have been proposed to improve nonrigid registration techniques for multimodal breast image alignment. A deformable breast model may also be useful for overcoming difficulties in interpreting 2D X-ray projections (mammograms) of 3D volumes (breast tissues). If a deformable model could accurately predict the shape changes that breasts undergo during mammography, then the model could serve to localize suspicious masses (visible in mammograms) in the unloaded state, or in any other deformed state required for further investigations (such as biopsy or other medical imaging modalities). In this paper, we present a validation study that was conducted in order to develop a biomechanical model based on the well-established theory of continuum mechanics (finite elasticity theory with contact mechanics) and demonstrate its use for this application. Experimental studies using gel phantoms were conducted to test the accuracy in predicting mammographic-like deformations. The material properties of the gel phantom were estimated using a nonlinear optimization process, which minimized the errors between the experimental and the model-predicted surface data by adjusting the parameter associated with the neo-Hookean constitutive relation. Two compressions (the equivalent of cranio-caudal and medio-lateral mammograms) were performed on the phantom, and the corresponding deformations were recorded using a MRI scanner. Finite element simulations were performed to mimic the experiments using the estimated material properties with appropriate boundary conditions. The simulation results matched the experimental recordings of the deformed phantom, with a sub-millimeter root-mean-square error for each compression state. Having now validated our finite element model of breast compression, the next stage is to apply the model to clinical images.
Publisher: Springer Berlin Heidelberg
Date: 2008
DOI: 10.1007/978-3-540-85990-1_91
Abstract: We have developed a biomechanical model of the breast to simulate compression during mammographic imaging. The modelling framework was applied to a set of MR images of the breasts of a volunteer. Images of the uncompressed breast were segmented into skin and pectoral muscle, from which a finite element (FE) mesh of the left breast was generated using a nonlinear geometric fitting process. The compression plates within the breast MR coil were used to compress the volunteer's breasts by 32% in the latero-medial direction and the compressed breasts were subsequently imaged using MRI. The FE geometry of the uncompressed left breast was used to numerically simulate compression based on finite deformation elasticity coupled with contact mechanics, and in idual-specific tissue properties. Accuracy of the simulated FE model was analysed by comparing the predicted surface data, and locations of three internal features within the compressed breast, with the equivalent experimental observations. Model predictions of the surface deformation yielded a RMS error of 1.5 mm. The Euclidean errors in predicting the locations of three internal features were 4.1 mm, 4.1 mm and 6.5 mm. Whilst the model reliably reproduced the compressive deformation, further investigations are required in order to test the validity of the underlying modelling assumptions. A reliable biomechanical model will provide a multi-modality imaging registration tool to help identify potential tumours observed between mammograms and other imaging modalities such as MRI or ultrasound.
Publisher: Springer New York
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Wiley
Date: 25-03-2011
DOI: 10.1002/CNM.1441
Publisher: Elsevier BV
Date: 2008
DOI: 10.1016/J.JBIOMECH.2007.07.016
Abstract: Mammography is currently the most widely used screening and diagnostic tool for breast cancer. Because X-ray images are 2D projections of a 3D object, it is not trivial to localise features identified in mammogram pairs within the breast volume. Furthermore, mammograms represent highly deformed configurations of the breast under compression, thus the tumour localisation process relies on the clinician's experience. Biomechanical models of the breast undergoing mammographic compressions have been developed to overcome this limitation. In this study, we present the development of a modelling framework that implements Coulomb's frictional law with a finite element analysis using a C(1)-continuous Hermite mesh. We compared two methods of this contact mechanics implementation: the penalty method, and the augmented Lagrangian method, the latter of which is more accurate but computationally more expensive compared to the former. Simulation results were compared with experimental data from a soft silicon gel phantom in order to evaluate the modelling accuracy of each method. Both methods resulted in surface-deformation root-mean-square errors of less than 2mm, whilst the maximum internal marker prediction error was less than 3mm when simulating two mammographic-like compressions. Simulation results were confirmed using the augmented Lagrangian method, which provided similar accuracy. We conclude that contact mechanics on soft elastic materials using the penalty method with an appropriate choice of the penalty parameters provides sufficient accuracy (with contact constraints suitably enforced), and may thus be useful for tracking breast tumours between clinical images.
Publisher: AIP Publishing
Date: 06-2023
DOI: 10.1063/5.0141890
Abstract: Atrial and ventricular fibrillation (AF/VF) are characterized by the repetitive regeneration of topological defects known as phase singularities (PSs). The effect of PS interactions has not been previously studied in human AF and VF. We hypothesized that PS population size would influence the rate of PS formation and destruction in human AF and VF, due to increased inter-defect interaction. PS population statistics were studied in computational simulations (Aliev–Panfilov), human AF and human VF. The influence of inter-PS interactions was evaluated by comparison between directly modeled discrete-time Markov chain (DTMC) transition matrices of the PS population changes, and M/M/∞ birth-death transition matrices of PS dynamics, which assumes that PS formations and destructions are effectively statistically independent events. Across all systems examined, PS population changes differed from those expected with M/M/∞. In human AF and VF, the formation rates decreased slightly with PS population when modeled with the DTMC, compared with the static formation rate expected through M/M/∞, suggesting new formations were being inhibited. In human AF and VF, the destruction rates increased with PS population for both models, with the DTMC rate increase exceeding the M/M/∞ estimates, indicating that PS were being destroyed faster as the PS population grew. In human AF and VF, the change in PS formation and destruction rates as the population increased differed between the two models. This indicates that the presence of additional PS influenced the likelihood of new PS formation and destruction, consistent with the notion of self-inhibitory inter-PS interactions.
Publisher: Springer Berlin Heidelberg
Date: 2007
DOI: 10.1007/978-3-540-75757-3_79
Abstract: Breast cancer detection, diagnosis and treatment increasingly involves images of the breast taken with different degrees of breast deformation. We introduce a new biomechanical modelling framework for predicting breast deformation and thus aiding the combination of information derived from the various images. In this paper, we focus on MR images of the breast under different loading conditions, and consider methods to map information between the images. We generate subject-specific finite element models of the breast by semi-automatically fitting geometrical models to segmented data from breast MR images, and characterizing the subject-specific mechanical properties of the breast tissues. We identified the unloaded reference configuration of the breast by acquiring MR images of the breast under neutral buoyancy (immersed in water). Such imaging is clearly not practical in the clinical setting, however this previously unavailable data provides us with important data with which to validate models of breast biomechanics, and provides a common configuration with which to refer and interpret all breast images. We demonstrate our modelling framework using a pilot study that was conducted to assess the mechanical performance of a subject-specific homogeneous biomechanical model in predicting deformations of the breast of a volunteer in a prone gravity-loaded configuration. The model captured the gross characteristics of the breast deformation with an RMS error of 4.2 mm in predicting the skin surface of the gravity-loaded shape, which included tissue displacements of over 20 mm. Internal tissue features identified from the MR images were tracked from the reference state to the prone gravity-loaded configuration with a mean error of 3.7 mm. We consider the modelling assumptions and discuss how the framework could be refined in order to further improve the tissue tracking accuracy.
Publisher: Wiley
Date: 09-04-2010
DOI: 10.1002/WSBM.58
Abstract: Biomechanical modeling of the breast is a burgeoning research field that has potential uses across a wide range of healthcare applications. This review describes recent developments regarding multi-modal breast image analysis, and outlines some of the key challenges that researchers face in introducing the models into the clinical arena. Deformable breast models have demonstrated capabilities across a wide range of breast cancer diagnoses and treatments. Specific applications include magnetic resonance (MR) image guided surgery, registration of x-ray and MR images, and breast reduction/augmentation surgery planning. Challenges lie in improving the fidelity of these models, which are presently simplistic and use many unverified parameters. Specific challenges include characterization of in idual-specific mechanical properties of breast tissues, precise representation of loading and boundary constraints during different clinical procedures, and validation of modeling techniques used to represent key mechanical aspects such as the suspensory Cooper's ligaments. Scientists must also work towards translating their research tools into the clinical setting by developing efficient tools with user-friendly interactivity. Widespread adoption of such techniques has the potential to significantly reduce the numbers of misdiagnosed breast cancers and enhance surgical planning for patient treatment.
Publisher: Springer Berlin Heidelberg
Date: 2011
DOI: 10.1007/8415_2011_92
Publisher: Elsevier BV
Date: 08-2009
Publisher: Elsevier BV
Date: 12-2013
DOI: 10.1016/J.MEDIA.2013.05.011
Abstract: This paper presents a novel X-ray and MR image registration technique based on in idual-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.
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Public Library of Science (PLoS)
Date: 11-07-2011
Publisher: ACM
Date: 09-10-2023
Publisher: IEEE
Date: 08-2006
Publisher: Wiley
Date: 2007
DOI: 10.1002/NME.2045
Publisher: Wiley
Date: 31-03-2011
Publisher: Springer Science and Business Media LLC
Date: 18-08-2011
DOI: 10.1007/S10549-011-1737-2
Abstract: Detailed knowledge of the lymphatic drainage of the breast is limited. Lymphoscintigraphy is a technique used during breast cancer treatment to accurately map patterns of lymphatic drainage from the primary tumour to the draining lymph nodes. This study aimed to create a statistical model to analyse the spread of breast cancer and primary tumour location using a large lymphoscintigraphy database, and visualise the results with a novel computational model. This study was based on lymphoscintigraphy data from 2,304 breast cancer patients treated at the Royal Prince Alfred Hospital Medical Centre in Sydney, Australia. Bayesian inferential techniques were implemented to estimate the probabilities of lymphatic drainage from each region of the breast to each draining node field, to multiple node fields, and to determine probabilities of tumour prevalence in each breast region. A finite element model of the torso and discrete model of the draining node fields were created to visualise these data and a software tool was developed to display the results ( www.abi.auckland.ac.nz/breast-cancer ). Results confirmed that lymphatic drainage is most likely to occur to the axillary node field, and that there is significant likelihood of drainage to the internal mammary node field. The likelihood of lymphatic drainage from the whole breast to the axillary, internal mammary, infraclavicular, supraclavicular and interpectoral node fields were 98.2, 35.3, 1.7, 3.1, and 0.7%, respectively whilst the probability of lymphatic drainage to multiple node fields was estimated to be 36.4%. Additionally, primary tumours are most likely to develop in the upper regions of the breast. The models developed provide quantitative estimates of lymphatic drainage of the breast, giving important insights into understanding breast cancer metastasis and have the potential to benefit both clinicians and patients during breast cancer diagnosis and treatment.
Publisher: Springer New York
Date: 2010
Publisher: Springer New York
Date: 2011
Publisher: Springer New York
Date: 2010
Start Date: 2020
End Date: 12-2024
Amount: $750,000.00
Funder: Australian Research Council
View Funded Activity