An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment.

Clinicians face many challenges when diagnosing and treating breast cancer. These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different treatment procedures. We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling and population-based statistical analysis to assist clinicians during breast cancer detection and treatment procedures. This paper summarizes our recent research to address the various technical and implementation challenges associated with creating a fully automated system. The workflow is applied to predict the repositioning of tumours from the prone position, where diagnostic magnetic resonance imaging is performed, to the supine position where treatment procedures are performed. We discuss our recent advances towards addressing challenges in identifying the mechanical properties of the breast and evaluating the accuracy of the biomechanical models. We also describe our progress in implementing a prototype of this workflow in clinical practice. Clinical adoption of these state-of-the-art modelling techniques has significant potential for reducing the number of misdiagnosed breast cancers, while also helping to improve the treatment of patients.

Quantifying normal geometric variation in human pulmonary lobar geometry from high resolution computed tomography.

Previous studies of the ex vivo lung have suggested significant intersubject variability in lung lobe geometry. A quantitative description of normal lung lobe shape would therefore have value in improving the discrimination between normal population variability in shape and pathology. To quantify normal human lobe shape variability, a principal component analysis (PCA) was performed on high resolution computed tomography (HRCT) imaging of the lung at full inspiration. Volumetric imaging from 22 never-smoking subjects (10 female and 12 male) with normal lung function was included in the analysis. For each subject, an initial finite element mesh geometry was generated from a group of manually selected nodes that were placed at distinct anatomical locations on the lung surface. Each mesh used cubic shape functions to describe the surface curvilinearity, and the mesh was fitted to surface data for each lobe. A PCA was performed on the surface meshes for each lobe. Nine principal components (PCs) were sufficient to capture >90% of the normal variation in each of the five lobes. The analysis shows that lobe size can explain between 20% and 50% of intersubject variability, depending on the lobe considered. Diaphragm shape was the next most significant intersubject difference. When the influence of lung size difference is removed, the angle of the fissures becomes the most significant shape difference, and the variability in relative lobe size becomes important. We also show how a lobe from an independent subject can be projected onto the study population's PCs, demonstrating potential for abnormalities in lobar geometry to be defined in a quantitative manner.

Development of a 3D finite element model of lens microcirculation.

BACKGROUND: It has been proposed that in the absence of a blood supply, the ocular lens operates an internal microcirculation system. This system delivers nutrients, removes waste products and maintains ionic homeostasis in the lens. The microcirculation is generated by spatial differences in membrane transport properties; and previously has been modelled by an equivalent electrical circuit and solved analytically. While effective, this approach did not fully account for all the anatomical and functional complexities of the lens. To encapsulate these complexities we have created a 3D finite element computer model of the lens. METHODS: Initially, we created an anatomically-correct representative mesh of the lens. We then implemented the Stokes and advective Nernst-Plank equations, in order to model the water and ion fluxes respectively. Next we complemented the model with experimentally-measured surface ionic concentrations as boundary conditions and solved it. RESULTS: Our model calculated the standing ionic concentrations and electrical potential gradients in the lens. Furthermore, it generated vector maps of intra- and extracellular space ion and water fluxes that are proposed to circulate throughout the lens. These fields have only been measured on the surface of the lens and our calculations are the first 3D representation of their direction and magnitude in the lens. CONCLUSION: Values for steady state standing fields for concentration and electrical potential plus ionic and fluid fluxes calculated by our model exhibited broad agreement with observed experimental values. Our model of lens function represents a platform to integrate new experimental data as they emerge and assist us to understand how the integrated structure and function of the lens contributes to the maintenance of its transparency.