Understanding and quantifying the mechanical properties of breast tissues has been a subject of interest for the past two decades. This has been motivated in part by interest in modelling soft tissue response for surgery planning and virtual-reality-based surgical training. Interpreting elastography images for diagnostic purposes also requires a sound understanding of normal and pathological tissue mechanical properties. Reliable data on tissue elastic properties are very limited and those which are available tend to be inconsistent, in part as a result of measurement methodology. We have developed specialized techniques to measure tissue elasticity of breast normal tissues and tumour specimens and applied them to 169 fresh ex vivo breast tissue samples including fat and fibroglandular tissue as well as a range of benign and malignant breast tumour types. Results show that, under small deformation conditions, the elastic modulus of normal breast fat and fibroglandular tissues are similar while fibroadenomas were approximately twice the stiffness. Fibrocystic disease and malignant tumours exhibited a 3-6-fold increased stiffness with high-grade invasive ductal carcinoma exhibiting up to a 13-fold increase in stiffness compared to fibrogalndular tissue. A statistical analysis showed that differences between the elastic modulus of the majority of those tissues were statistically significant. Implications for the specificity advantages of elastography are reviewed.

Screening with both MRI and mammography might rule out cancerous lesions better than mammography alone in women who are known or likely to have an inherited predisposition to breast cancer.

Breast MRI may be superior to mammography and ultrasound for the screening of women at high risk for hereditary breast cancer.

Over the past decade, several methods have been proposed to image tissue elasticity based on imaging methods collectively called elastography. While progress in developing these systems has been rapid, the basic understanding of tissue properties to interpret elastography images is generally lacking. To address this limitation, we developed a system to measure the Young's modulus of small soft tissue specimens. This system was designed to accommodate biological soft tissue constraints such as sample size, geometry imperfection and heterogeneity. The measurement technique consists of indenting an unconfined small block of tissue while measuring the resulting force. We show that the measured force-displacement slope of such a geometry can be transformed to the tissue Young's modulus via a conversion factor related to the sample's geometry and boundary conditions using finite element analysis. We also demonstrate another measurement technique for tissue elasticity based on quasi-static magnetic resonance elastography in which a tissue specimen encased in a gelatine-agarose block undergoes cyclical compression with resulting displacements measured using a phase contrast MRI technique. The tissue Young's modulus is then reconstructed from the measured displacements using an inversion technique. Finally, preliminary elasticity measurement results of various breast tissues are presented and discussed.

A quasistatic magnetic resonance elastography (MRE) method for the evaluation of breast cancer is proposed. Using a phase contrast, stimulated echo MRI approach, strain imaging in phantoms and volunteers is presented. First-order assessment of tissue biomechanical properties based on inverse strain mapping is outlined and demonstrated. The accuracy of inverse strain imaging is studied through simulations in a two-dimensional model and in an anthropomorphic, three-dimensional finite-element model of the breast. To improve the accuracy of modulus assessment by elastography, inverse methods are discussed as an extension to strain imaging, and simulations quantify MRE in terms of displacement signal/noise required for robust inversion. A direct inversion strategy providing information on tissue modulus and pressure distribution is described along with a novel iterative method utilizing a priori knowledge of tissue geometry. It is shown that through the judicious choice of information from previous contrast-enhanced MRI breast images, MRE data acquisition requirements can be significantly reduced while maintaining robust modulus reconstruction in the presence of strain noise. An experimental apparatus for clinical breast MRE and preliminary images of a normal volunteer are presented.

The results of this preliminary study suggest that contrast-enhanced digital mammography potentially may be useful in identification of lesions in the mammographically dense breast. Further investigation of contrast-enhanced digital mammography as a diagnostic tool for breast cancer is warranted.

Breast tumor diagnosis requires both high spatial resolution to obtain information about tumor morphology and high temporal resolution to probe the kinetics of contrast uptake. Adaptive sampling of k-space allows images in dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) to be reconstructed at various spatial or temporal resolutions from the same dataset. However, conventional radial approaches have limited flexibility that restricts image reconstruction to predetermined resolutions. Golden-angle radial k-space sampling achieves flexibility in-plane with samples that are incremented by the golden angle, which fills two-dimensional (2D) k-space with radial spokes that have a relatively uniform angular distribution for any time interval. We extend this method to threedimensional (3D) radial sampling, or 3D-Projection Reconstruction (3D-PR) using multidimensional golden means, which are derived from modified Fibonacci sequences by an eigenvalue approach. (1) and patients with hereditary breast cancer (HBC) have an 85% lifetime risk of contracting the disease (2). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a highly sensitive technique for detecting breast cancers (3-5), but specificity is somewhat lower (6). Diagnosis of breast lesions using DCE-MRI could potentially be made more accurate using images that have high spatial resolution to characterize lesion morphology, and high temporal resolution to probe the contrast kinetics of a lesion. However, there is an intrinsic trade-off between temporal and spatial resolution in MRI, and there is currently little agreement on an optimal balance of temporal and spatial resolution.Adaptive sampling of k-space data, first proposed by Song et al. (7), achieves both high spatial or high temporal resolution images from the same dataset by frequently sampling the central part of k-space that gives useful information regarding image contrast. Originally used for the purposes of time-resolved angiography, projection reconstruction-time-resolved imaging of contrast kinetics (PR-TRICKS) (8), is an example of a trajectory that samples 3D k-space in an adaptive manner. PR-TRICKS sampling allows both high temporal resolution (fast) images to be reconstructed from blocks of central k-space data, or high spatial resolution images to be obtained by including data from the periphery of k-space. Ramsay et al. (9) have applied PR-TRICKS imaging to breast DCE-MRI in an adaptive manner to reconstruct images at discrete temporal/spatial resolutions. Although fast images from PR-TRICKS contain useful image contrast, they are corrupted by aliasing artifacts that arise from undersampling. The temporal instability of these streaking artifacts over successive fast images can reduce the accuracy of physiological parameters derived from pharmacokinetic modelling of enhancement curves. Minimizing these undersampling artifacts is important to improving the diagnostic quality of high-temporal-resolution images.It has been shown that if the streaking artifacts are p...

Over the past decade, there has been increasing interest in modelling soft tissue deformation. This topic has several biomedical applications ranging from medical imaging to robotic assisted telesurgery. In these applications, tissue deformation can be very large due to low tissue stiffness and lack of physical constraints. As a result, deformation modelling of such organs often requires a treatment, which reflects nonlinear behaviour. While computational techniques such as nonlinear finite element methods are well developed, the required intrinsic nonlinear mechanical parameters of soft tissues that are critical to develop reliable tissue deformation models are not well known. To address this issue, we developed a system to measure the hyperelastic parameters of small ex vivo tissue samples. This measurement technique consists of indenting an unconfined small block of tissue using a computer controlled loading system while measuring the resulting indentation force. The nonlinear tissue force-displacement response is used to calculate the hyperelastic parameters via an appropriate inversion technique. This technique is based on a nonlinear least squares formulation that uses a nonlinear finite element model as the direct problem solver. The features of the system are demonstrated with two samples of breast tissue and typical hyperelastic results are presented.