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. A greater understanding of the differences in the shoulder orientation between prone and supine could improve the accuracy of breast biomechanical models. METHODS: 19 landmarks were placed on the sternum, clavicle, scapula, and humerus of the shoulder girdle in prone and supine breast MRIs (N = 10). These landmarks were used in an optimization framework to fit subject-specific skeletal models and compare joint angles of the shoulder girdle between these positions. FINDINGS: The mean Euclidean distance between joint locations from the fitted skeletal model and the manually identified joint locations was 15.7 mm ± 2.7 mm. Significant differences were observed between prone and supine. Compared to supine position, the shoulder girdle in the prone position had the lateral end of the clavicle in more anterior translation (i.e., scapula more protracted) (P < 0.05), the scapula in more protraction (P < 0.01), the scapula in more upward rotation (associated with humerus elevation) (P < 0.05); and the humerus more elevated (P < 0.05) for both the left and right sides. INTERPRETATION: Shoulder girdle orientation was found to be different between prone and supine. These differences would affect the shape of multiple pectoral muscles, which would affect breast shape and the accuracy of biomechanical models.

Assessing vaginal pressure profiles before and after prolapse surgery using an intravaginal pressure sensor (femfit®).

INTRODUCTION AND HYPOTHESIS: The impact of surgery on pelvic floor muscle (PFM) function remains uncertain. There is a pressure differential along the length of the vagina, influenced by surrounding active and passive tissue structures, giving rise to a pressure profile. The aim of this study is to determine if an intravaginal pressure sensor, femfit®, can measure differences in pressure profiles before and after surgery for pelvic organ prolapse (POP). METHODS: This pilot study includes 22 women undergoing POP surgery. Intravaginal pressure profiles were measured with femfit® pre- and post-surgery and differences tested using paired Student's t-tests. Patients completed validated questionnaires for vaginal, bowel, and urinary incontinence symptoms pre- and post-POP surgery and a femfit® usability questionnaire. RESULTS: Sixteen sets of vaginal pressure data were analysed. The highest pressure generated was identified as the peak PFM pressure, whilst all sensor measurements provided a pressure profile. Intra-abdominal pressure (IAP) was measured by the most distal sensor, 8. On average, the difference between peak PFM pressure and IAP was significantly greater post-surgery (p < 0.01). Urinary and vaginal symptom questionnaire scores were significantly improved after POP surgery. Femfit® usability questionnaires demonstrated high levels of patient acceptability. CONCLUSIONS: Women generate higher peak PFM pressures compared to IAP post-POP surgery, with pressure profiles that are comparable to women without POP. This metric might be useful to assess the outcome of POP surgery and encourage women to maintain this profile via PFM training, potentially reducing POP recurrence risk.

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.

Characterizing the ex vivo mechanical properties of synthetic polypropylene surgical mesh.

The use of synthetic polypropylene mesh for hernia surgical repair and the correction of female pelvic organ prolapse have been controversial due to increasing post-operative complications, including mesh erosion, chronic pain, infection and support failure. These morbidities may be related to a mismatch of mechanical properties between soft tissues and the mesh. The aim of this study was to gain a better understanding of the biomechanical behavior of Prolene polypropylene mesh (Ethicon, Sommerville, NJ, USA), which is widely used for a variety of surgical repair procedures. The stiffness and permanent deformation of Prolene mesh were compared in different directions by performing uniaxial tensile failure tests, cyclic and creep tests at simulated physiological loads in the coursewise (0°), walewise (90°) and the diagonal (45°) directions. Failure tests suggest that the mechanical properties of the mesh is anisotropic; with response at 0° being the most compliant while 90° was the stiffest. Irreversible deformation and viscoelastic behavior were observed in both cyclic and creep tests. The anisotropic property may be relevant to the placement of mesh in surgery to maximize long term mesh performance. The considerable permanent deformation may be associated with an increased risk of post-operative support failure.

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.

Simulating the three-dimensional deformation of in vivo facial skin.

Characterising the mechanical properties of human facial skin is a challenging but important endeavour with applications in biomedicine, surgery simulation, forensics, and animation. Many existing computer models of the face are not based on in vivo facial skin deformation data but rather on experiments using in vitro facial skin or other soft tissues. The facial skin of five volunteers was subjected to a rich set of deformations using a micro-robotic device. The force-displacement response was recorded for each deformation. All volunteers' facial skin exhibited a non-linear, anisotropic, and viscoelastic force-displacement response. We propose a finite element model that simulated the experimental deformations with error-of-fits ranging from 11% to 23%. The skin was represented by an Ogden strain energy function and a quasi-linear viscoelastic law. From non-linear optimisation procedures, we determined material parameters and in vivo pre-stresses for the central cheek area of five volunteers and five other facial points on one volunteer. Pre-stresses ranged from 15.9kPa to 89.4kPa.

Predicting lymphatic drainage patterns and primary tumour location in patients with breast cancer.

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.

Lymphatic drainage and tumour prevalence in the breast: a statistical analysis of symmetry, gender and node field independence.

Current understanding of the lymphatics draining the breast is controversial, despite its known importance in the spread of breast cancer. Similarly, knowledge regarding the spatial distribution of primary tumours in the breast is limited. This study sought to test commonly held assumptions in this field, including: (i) that breast lymphatic drainage and tumour prevalence are symmetric between the left and right sides of the body, (ii) that males and females have the same drainage patterns and tumour prevalences, and (iii) that lymphatic drainage in the breast occurs independently to different node fields. This study has used lymphoscintigraphy data from 2304 breast cancer patients treated at the RPAH Medical Centre, Sydney, Australia. Symmetry of lymphatic drainage and tumour distribution as well as gender differences were tested using Fisher's exact test. Drainage independence was assessed using Fisher's exact test, and a multivariate probit model was used to test for drainage correlations. Results showed that the breasts are likely to have symmetric lymphatic drainage and tumour prevalence, and that there is no significant difference between males and females. Furthermore, results showed that direct lymphatic drainage of the breasts is likely to be independent between node fields. Collectively, these results serve to further our understanding of lymphatic anatomy and the distribution of tumours in the breast.

Stress development, heat production and dynamic modulus of rat isolated cardiac trabeculae revealed in a flow-through micro-mechano-calorimeter.

Progress toward understanding the thermo-mechanical behavior of isolated cardiac muscle, excised from either healthy or diseased heart, is contingent on being able to measure simultaneously the stress (force per cross-sectional area) and heat production. Determination of dynamic modulus (dynamic stiffness times muscle length per cross-sectional area) sheds further light on the behavior of the force-and heat-generating actin-myosin cross-bridges. We are in a unique position to perform such measurements, given the recent completion of a micro-mechano-calorimeter. In this paper, we characterize the micro-mechano-calorimeter and present experimental results of twitch stress, heat per twitch and dynamic modulus measured in rat right-ventricular trabeculae at varied stimulus frequencies and muscle lengths. The minute radial dimensions of cardiac trabeculae (which approximate those of a human hair) ensure adequate provision of oxygen and nutrients via diffusion from the continuously replenished superfusate flowing through the measurement chamber. This enables investigation of the thermo-mechanical performance of cardiac trabeculae for many hours.

Modeling breast biomechanics for multi-modal image analysis--successes and challenges.

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 individual-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.

Modelling mammographic compression of the breast.

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 individual-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.

Creating individual-specific biomechanical models of the breast for medical image analysis.

RATIONALE AND OBJECTIVES: 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 individual. We demonstrate the framework's capabilities by creating models of the left breasts of two volunteers and tracking their deformations across MRIs. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Frictional contact mechanics methods for soft materials: application to tracking breast cancers.

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.

Towards tracking breast cancer across medical images using subject-specific biomechanical models.

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.