Breast cancer is a leading cause of death in women. Tumours are usually detected by palpation or X-ray mammography
followed by further imaging, such as magnetic resonance imaging (MRI) or ultrasound. The aim of this research is to
develop a biophysically-based computational tool that will allow accurate collocation of features (such as suspicious
lesions) across multiple imaging views and modalities in order to improve clinicians' diagnosis of breast cancer. We
have developed a computational framework for generating individual-specific, 3D finite element models of the breast.
MR images were obtained of the breast under gravity loading and neutrally buoyant conditions. Neutrally buoyant breast
images, obtained whilst immersing the breast in water, were used to estimate the unloaded geometry of the breast (for
present purposes, we have assumed that the densities of water and breast tissue are equal). These images were segmented
to isolate the breast tissues, and a tricubic Hermite finite element mesh was fitted to the digitised data points in order to
produce a customized breast model. The model was deformed, in accordance with finite deformation elasticity theory, to
predict the gravity loaded state of the breast in the prone position. The unloaded breast images were embedded into the
reference model and warped based on the predicted deformation. In order to analyse the accuracy of the model
predictions, the cross-correlation image comparison metric was used to compare the warped, resampled images with the
clinical images of the prone gravity loaded state. We believe that a biomechanical image registration tool of this kind
will aid radiologists to provide more reliable diagnosis and localisation of breast cancer.
Research has suggested that athletes involved in high-intensity sports for sustained periods have a higher probability
of experiencing prolonged second stage of labour compared to non-athletes. The mechanism responsible for this complication is unknown but may depend on the relative size or tone of the pelvic floor muscles. Prolonged training can result in enlargement and stiffening of these muscles, providing increased resistance as the fetal head descends through the birth canal during a vaginal birth. On the other hand, recent studies have suggested an association between increased muscle bulk in athletes and higher distensibility. This project aims to use mathematical modelling to study the relationship between the size and tone of the pelvic floor muscles and the level of difficulty during childbirth. We obtained sets of magnetic resonance (MR) images of the pelvic floor region for a female athlete and a female non-athlete. Thirteen components of the pelvic floor were segmented and used to generate finite element (FE) models. The fetal head data was obtained by laser scanning a skull replica and a FE model was fitted to these data. We used contact mechanics to simulate the motion of the fetal head moving through the pelvic floor, constructed from the non-athlete data. A maximum stretch ratio of 3.2 was induced in the muscle at the left lateral attachment point to the pubis. We plan to further improve our modelling framework to include active muscle contraction and fetal head rotations in order to address the hypotheses that there is a correlation between the level of difficulty and the size or tone of the pelvic floor muscles.
Mammography is currently recognized as the gold standard for screening and diagnosis of breast cancer. A number of non-rigid registration algorithms have been used to track regions of interest across 2D mammographic images (cranio-caudal and mediolateral-oblique views). However, such techniques typically rely solely on the image properties A modeling framework is presented to potentially improve tumor tracking by constraining the image registration using physical laws of soft tissue mechanics. A simplified phantom model was constructed using an incompressible, homogeneous and isotropic silicon gel, modeled as a hyperelastic neo-Hookean material. The material constant was estimated using a nonlinear least-squares optimization technique to minimize errors between predicted displacements of material points in a large deformation finite element (FE) model and the corresponding experimentally observed displacements under gravity loading. The gel phantom was compressed between two plates to mimic a typical mammographic procedure and the deformed surfaces were scanned. Contact constraints were used to simulate compression in the FE model and the predicted displacements agreed well with the experimentally observed deformation. We also found that the effects of gravity markedly affected the accuracy of the compression model results. We conclude that modeling the soft tissue mechanics of the breast can provide a useful tool for tracking possible tumors from the compressed state (during mammography) to other configurations for further examination.