An interactive 3D atlas of sentinel lymph nodes in breast cancer developed using SPECT/CT.

Background: The identification and assessment of sentinel lymph nodes (SLNs) in breast cancer is important for optimised patient management. The aim of this study was to develop an interactive 3D breast SLN atlas and to perform statistical analyses of lymphatic drainage patterns and tumour prevalence.

Methods: A total of 861 early-stage breast cancer patients who underwent preoperative lymphoscintigraphy and SPECT/CT were included.

Quantifying changes in shoulder orientation between the prone and supine positions from magnetic resonance imaging.

Background: Predicting breast tissue motion using biomechanical models can provide navigational guidance during breast cancer treatment procedures. These models typically do not account for changes in posture between procedures. Difference in shoulder position can alter the shape of the pectoral muscles and breast.

Vision for the 12 LABOURS Digital Twin Platform.

Clinical translation of personalised computational physiology workflows and digital twins can revolutionise healthcare by providing a better understanding of an individual's physiological processes and any changes that could lead to serious health consequences. However, the lack of common infrastructure for developing these workflows and digital twins has hampered the realisation of this vision. The Auckland Bioengineering Institute's 12 LABOURS project aims to address these challenges by developing a Digital Twin Platform to enable researchers to develop and personalise computational physiology models to an individual's health data in clinical workflows.

Toward Development of a Portable System for 3D Fluorescence Lymphography.

Lymphoedema is a debilitating disease that results in chronic swelling of a body region due to a dysfunctional lymphatic system. Since a cure is yet to be identified for this disease, management is currently the best option for preventing disease progression and improving patient outcomes. Fluorescence lymphography is a popular approach for mapping the lymphatic vessels to provide information about the underlying lymphatic dysfunction.

Roadmap for an imaging and modelling paediatric study in rural NZ.

Our study methodology is motivated from three disparate needs: one, imaging studies have existed in silo and study organs but not across organ systems; two, there are gaps in our understanding of paediatric structure and function; three, lack of representative data in New Zealand. Our research aims to address these issues in part, through the combination of magnetic resonance imaging, advanced image processing algorithms and computational modelling. Our study demonstrated the need to take an organ-system approach and scan multiple organs on the same child.

MITEA: A dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging.

Segmentation of the left ventricle (LV) in echocardiography is an important task for the quantification of volume and mass in heart disease. Continuing advances in echocardiography have extended imaging capabilities into the 3D domain, subsequently overcoming the geometric assumptions associated with conventional 2D acquisitions. Nevertheless, the analysis of 3D echocardiography (3DE) poses several challenges associated with limited spatial resolution, poor contrast-to-noise ratio, complex noise characteristics, and image anisotropy.

Automated model calibration with parallel MCMC: Applications for a cardiovascular system model.

Computational physiological models continue to increase in complexity, however, the task of efficiently calibrating the model to available clinical data remains a significant challenge. One part of this challenge is associated with long calibration times, which present a barrier for the routine application of model-based prediction in clinical practice. Another aspect of this challenge is the limited available data for the unique calibration of complex models.

High-Resolution Spatiotemporal Quantification of Intestinal Motility With Free-Form Deformation.

Objective: To develop a method to quantify strain fields from in vivo intestinal motility recordings that mitigate accumulation of tracking error.

Methods: The deforming geometry of the intestine in video sequences was modeled by a biquadratic B-spline mesh. Green-Lagrange strain fields were computed to quantify the surface deformations.

Efficient Ventricular Parameter Estimation Using AI-Surrogate Models.

The onset and progression of pathological heart conditions, such as cardiomyopathy or heart failure, affect its mechanical behaviour due to the remodelling of the myocardial tissues to preserve its functional response. Identification of the constitutive properties of heart tissues could provide useful biomarkers to diagnose and assess the progression of disease. We have previously demonstrated the utility of efficient AI-surrogate models to simulate passive cardiac mechanics.

Efficient estimation of load-free left ventricular geometry and passive myocardial properties using principal component analysis.

Models of cardiac mechanics require a well-defined reference geometry from which deformations and hence myocardial strain and stress can be calculated. In the in vivo beating heart, the load-free (LF) geometry generally cannot be measured directly, since, in many cases, there is no stage at which the lumen pressures and contractile state are all zero. Therefore, there is a need for an efficient method to estimate the LF geometry, which is essential for an accurate mechanical simulation of left ventricular (LV) mechanics, and for estimations of passive and contractile constitutive parameters of the heart muscle.

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.

Surface deformation tracking and modelling of soft materials.

Many computer vision algorithms have been presented to track surface deformations, but few have provided a direct comparison of measurements with other stereoscopic approaches and physics-based models. We have previously developed a phase-based cross-correlation algorithm to track dense distributions of displacements over three-dimensional surfaces. In the present work, we compare this algorithm with one that uses an independent tracking system, derived from an array of fluorescent microspheres.

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.

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.

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.