Elasto-mammography: Theory, Algorithm, and Phantom Study

A new imaging modality framework, called elasto-mammography, is proposed to generate the elastograms of breast tissues based on conventional X-ray mammography. The displacement information is extracted from mammography projections before and after breast compression. Incorporating the displacement measurement, an elastography reconstruction algorithm is specifically developed to estimate the elastic moduli of heterogeneous breast tissues. Case studies with numerical breast phantoms are conducted to demonstrate the capability of the proposed elasto-mammography. Effects of noise with measurement, geometric mismatch, and elastic contrast ratio are evaluated in the numerical simulations. It is shown that the proposed methodology is stable and robust for characterization of the elastic moduli of breast tissues from the projective displacement measurement

Automated breast cancer diagnostics using a Digital Image Elasto Tomography (DIET) system.

Breast cancer was the most common cancer in the world in 2020 and is a major worldwide health concern. For women it accounts for a quarter of all cancer cases with high mortality of 680,000 annual deaths in women. Screening programs are necessary to reduce breast cancer mortality by finding cancer at an earlier, more curable stage, increasing treatment options, reducing treatment costs, and improving outcomes. Ideally, screening would equitably cover women of all ages, as 2% and 11% of breast cancer occurs in women under 30 and 40 years of age, respectively. Currently, the only approved large-scale breast screening technology is x-ray mammography, which has a number of issues making it unsuitable as an equitable breast screening tool. Limitations of mammography include painful breast compression, harmful radiation exposure and poorer performance in younger women and women with dense breast tissue (50% of women). Small radio-density contrast results in radiologist-dependent performance and a high false positive rate. In addition, mammography is expensive and requires infrastructure, such as x-ray shielded rooms, making it unsuitable for screening programs in developing countries and much less accessible for many women who live rurally. These issues contribute to a lower screening participation, as well as no publicly funded screening solution for younger women, which leads to cancer being found at later stages with consequently worse outcomes. This thesis presents a breast screening technology capable of overcoming these screening limitations and develops clinically feasible, automated diagnostic algorithms to be used in con- junction with this technology to provide higher diagnostic accuracy than mammography. First, the need for a different breast screening technology is identified by analysing the 2004 Kew Zealand change in screening eligibility age and using this analysis to quantify the socio-economic benefits of providing a more equitable breast screening solution. This analysis clearly showed the high number of women found with larger tumors when they initially enter into screening, where previously cancer has grown unchecked. This incidence of larger tumors highlights inequity based on age, where younger women contracting breast cancer are more likely to have worse outcomes and lower survival than those eligible for screening. Ethnic inequities also exist with M-aori women, the indigenous people of Kew Zealand, who are 21% more likely to be diagnosed with breast cancer than non-M-aori, and 68% more likely to die from it. Worse outcomes for M-aori are likely a result of lower breast screening participation due to a large portion of M-aori living rurally, away from main centres were mammography is exclusively provided, which demonstrates inequity based on access. Further, a 'diffi cult' history of poor interaction between M-aori and health providers means some may skip screening and/or experience inequity due to current or post racial bias. Quantification of socio-economic benefits showed increasing screening age eligibility to all women 20+ could potentially save 43 lives (∼7%) annually and reduce treatment costs by 10.1%. Increasing screening participation for the currently screened age group (45-69) to 90% could save 37 lives (∼5.5%) annually and reduce costs by 14.5%. Implementing a screening device with the capacity for both increased screening participation and expansion of screening age eligibility could save 102 lives (∼16%) annually and result in a 33.1% reduction in breast cancer treatment costs. Thus, significant social and economic benefits could be realised with the use of a new screening technology capable of overcoming mammography's limitations, such as increasing access and providing safe screening for all women. Second, this thesis suggests a technology capable of providing these socio-economic benefits and critically analyses the diagnostic accuracy of mammography to generate diagnostic criteria to assess diagnostic algorithms. Digital Image Elasto Tomography (DIET) has been developed as a breast screening technology to overcome limitations of mammography. The technology is portable, low-cost and with no requirement for additional infrastructure, making it suitable for use in any clinic and giving it the ability to increase access to breast screening for all women. Further, DIET testing is non-invasive with no harmful radiation exposure and is thus suitable for women of all ages. Breast screening using DIET is comfortable involving a women lying face down, with low-amplitude steady state vibration of one free hanging breast. Surrounding cameras capture images of breast surface motion, which are converted to displacement data using surface volume and optical flow techniques. Diagnostic analysis uses this displacement data to identify underlying breast tissue properties, such as stiffness and damping. Cancerous tissue is 400∼1000% stiffer than healthy tissue and, as such, can provide much higher diagnostic contrast than radio-density used in mammography (5∼10%). Diagnostic algorithms are able to be fully automated, removing requirements for skilled personnel to interpret diagnostic images, providing more consistent diagnosis. Critical analysis of mammography's diagnostic accuracy identified a number of issues with studies including using cohorts inclusive of larger palpable and/or prevalent tumors, resulting in disproportionately high sensitivity. Furthermore, accuracy based on interval cancers presenting between screens is flawed and highly dependent on screening interval resulting in higher than true sensitivity and specificity. The most sound methodology for assessing accuracy came from mammography studies, which compared mammography to other breast imaging modalities, such as ultrasound or MRI, resulting in average sensitivity and specificity values of 60% and 80%, respectively. These values determined the first criteria used to assess the diagnostic performance of DIET diagnostic algorithms, the second was to achieve a highly sensitive diagnostic with sensitivity and specificity 80% and 65%, respectively. A clinical trial involving 14 patients (28 breasts, 13 cancerous, 15 healthy) was carried out using the DIET technology following mammography screening. This clinical cohort is varied breast sizes from 200-1100cm3 and tumor diameters from 7-48 mm, and was used to test di- agnostic algorithms presented in this thesis. This unique clinical dataset is used to test novel diagnostic algorithms presented in this thesis. The core of this thesis presents diagnostic algorithms capable of providing higher diagnostic accuracy than mammography using DIET to realise the many potential benefits of this safe, portable, and non-invasive screening technology. The first diagnostic method uses a model developed in this thesis, based of Rayleigh damping, to describe the viscous damping distribution in the breast. The computationally efficient diagnostic algorithm segments the breast into four radial segments and fits this viscous damping model (VDM) to the viscous damping distribution of reference points in each segment. One model coefficient, related to stiffness, was then compared between segments using percentage thresholds, with healthy breasts hypothesised to have similar coefficient values between segments and cancerous breasts expected to have more varied coefficient values, indicative of a tumor. Twelve breast segmentation configurations were tested to ensure robustness. The optimal configuration, located on the outer more neutral side of the breast, resulted in optimal sensitivity and specificity of 80% and 75%, respectively, with a receiver operator characteristic (ROC) curve area (A-CC) of 0.84. The second diagnostic algorithm involves assessment of response frequency with higher frequency response associated with increased stiffness, and potential tumor presence. A similar segmentation methodology, with increased breast segmentation in the vertical direction was used to attempt to increase diagnostic resolution of smaller tumors. Second dominant response frequencies for all reference points in each segment were averaged, providing frequency component magnitudes were sufficient. Mean frequencies in segments around each vertical band were compared using diagnostic tolerances with segments outside the tolerance indicating high variability in frequency composition and, thus, potential cancer. As with the VDM method, twelve segmentation configurations were tested. Optimal configuration resulted in sensitivity and specificity of 81% and 75%, respectively and ROC curve A-CC of 0.85. Further combining of diagnostic results from two segments on opposite sides of the breast resulted in 100% sensitivity and 69% specificity. All diagnostic criteria were exceeded in both methods and diagnostic accuracy exceeds mammography. Finally, two clinically feasible methods were developed to combine and optimise these individual, tissue mechanics based diagnostic algorithms. The first uses opposite configurations in the frequency decomposition (FD) method. Consistent diagnosis in the two configurations are considered true and inconsistent results are diagnosed using the VDM method. This method results in sensitivity and specificity of 92% and 86%, respectively. The second combined method uses DIET measured breast volume to dictate the method used with small breast volumes diagnosed using the VDM method and large breast volumes diagnosed using the FD method. This method gives 100% sensitivity and 80% specificity. These clinically feasible methods show further diagnostic improvements resulting in a high diagnostic accuracy for this proof-of-concept clinical cohort. These diagnostic algorithms and optimal combinations prove high diagnostic accuracy using DIET can be achieved. This thesis shows implementation of DIET into breast screening programs could provide fast, automated breast cancer diagnosis and consequently faster treatment, improving outcomes and lowering treatment costs. DIET can increase equity for younger women, and positively impact breast screening for M-aori women and women living rurally, with portability enabling mobile screening services to reduce physical barriers to receiving breast care and increase equity of access. These benefits will also impact breast screening programs in developing countries, where mammography is not feasible due to excessive infrastructure cost and requirement for trained radiologists and technicians. Overall, this thesis quantifies the need and potential benefits of a new breast screening solution and critically analyses the diagnostic accuracy presented by mammography, identifying issues of infiated sensitivity values, which are commonly believed to be valid. Most importantly, this thesis develops automated diagnostic methods including combined clinically feasible algorithms, resulting in sensitivity and specificity, which far exceed mammography in this cohort. These diagnostic methods take DIET from a technology with undisputed benefits in terms of screening procedure, to a clinically feasible technology with proven diagnostic potential, thus, worthy of further research and investment

Elastographie-IRM pour le diagnostic et la caractérisation des lésions du sein

L élastographie-IRM du sein (MRE) est une technique d imagerie fonctionnelle non invasive utilisant les propriétés visco-élastiques des tissus et qui permet comme en élastographie-échographie d évaluer la rigidité d une lésion. Il est également possible, à la différence de l élastographie-échographie, d évaluer le degré de viscosité d une lésion, et ainsi grâce à la combinaison élasticité/viscosité, comparée à l analyse des paramètres IRM classiques comme la morphologie ou la cinétique de rehaussement, d améliorer la caractérisation lésionnelle. Très peu d études en élastographie-IRM du sein ont été menées à ce jour, essentiellement du fait d une problématique instrumentale et de mise à disposition d une antenne dédiée sein équipé d un dispositif de génération des ondes de cisaillement dans le sein. Dans un premier temps, nous avons pu établir et optimiser une séquence élasto-IRM du sein sur une série de 10 volontaires saines. Cette séquence basée sur un principe de séquence Spin Echo EPI-MRE 3D, a permis l acquisition de 50 coupes en 10 minutes sur un sein, compatible avec la pratique clinique en IRM du sein. Une approche multifréquence à 37,5 Hz, 75 Hz et 112,5 Hz a été ensuite testée sur les trois dernières volontaires puis transférées à notre population de patientes. Cette séquence multifréquence permettait la continuité de diffusion des ondes dans le sein. 50 patientes présentant des lésions indéterminées ou suspectes du sein (37 cancers, 13 bénins) ont ensuite été incluses dans ce protocole et examinées par IRM du sein classique avec séquence supplémentaire élasto-IRM. Certaines patientes étaient aussi examinées en élasto-échographie. Les données IRM morphologiques, dynamiques et de visco-élasticité IRM ont été corrélées à l histologie. Nous avons pu montrer que les paramètres visco-élastiques IRM étaient fortement corrélés avec le score de malignité d une lésion (Bi-RADS ACR) et avec le caractère différentiel bénin/malin. C est notamment le paramètre Gd qui représente l élasticité, qui était plus faible en cas de lésion suspecte BI-RADS 5. Le paramètre Gl était plus élevé dans les lésions malignes par rapport aux lésions bénignes, avec un niveau de viscosité statistiquement supérieur dans les lésions malignes. Le meilleur paramètre semble être le rapport y (Gl/Gd) qui était aussi significativement élevé dans les lésions malignes par comparaison avec les lésions bénignes du sein, et qui a été analysé comme un facteur indépendant. En pratique, l ajout de la séquence MRE à un examen IRM du sein classique a permis dans notre étude d améliorer significativement la sensibilité de l IRM (de 78 à 91 %) sans perte de spécificité, celle-ci étant initialement très bonne dans cette étude. Nous n avons pas en revanche établi de lien entre la fibrose, la quantification vasculaire ou la nécrose pour expliquer ces phénomènes de visco-élasticité des tumeurs. En conclusion, l élasto-IRM peut s avérer utile pour améliorer le diagnostic de lésions du sein en IRM. Une poursuite des travaux avec optimisation de la séquence pour qu elle puisse permettre l analyse des deux seins sera nécessaire pour sa diffusion en pratique clinique. Ce travail pourrait idéalement se poursuivre sur une plus grande série de patientes.MR-elastography (MRE) is a non-invasive functional Imaging technique using tissue mechanical visco-elastic properties to evaluate tissue stifness. MRE is different from elasticity Imaging in ultrasound, as it is possible to evaluate tumour viscosity. Combining viscosity and elasticity may improve MRI accuracy, in comparison with classical morphological and kinetics criteria. Only very few studies are focused on breast MRE, because of low availability of dedicated breast coils with MRE devices. Firstly, we developed and optimized a breast MRE sequence on a population of 10 volunteers. This sequence is based on a Spin Echo EPI-MRE 3D, and it was possible to acquire 50 slices on one breast in 10 minutes, which is applicable in a clinical routine in breast MRI. Secondly, a multi-frequency approach 37,5 Hz, 75 Hz and 112,5 Hz has been evaluated on the last three volunteers, then transferred to our patient s population. A continous diffusion of waves within the breast was possible with this multifrequency approach sequence. 50 patients presenting undetermined or suspicious breast lesions (37 cancers, 13 benign lesions) were included in this study and examined with a standard breast MRI and MRE sequence. Some patients were also examined with shear-wave ultrasound elastography (ARFI mode, Siemens ®). Morphological, kinetic and visco-elastic MR parameters were correlated to pathology. We demonstrated that MR visco-elastic properties were strongly correlated with Bi-RADS ACR malignancy score of a breast lesion and with malignant and benign status. The best parameter was Gd (dynamic modulus), which corresponded to lesion stiffness. Gd was lower in case of BI-RADS 5 lesions. Gl parameter (Loss modulus) was higher in malignant lesions in comparison with benign lesions, with viscosity level statistically higher in malignant lesions. The best criterion was the ratio y (Gl/Gd), which was significantly higher in malignant lesions in comparison with benign lesions; ratio y was statistically an independent factor. In practice, addition of a MRE sequence to a standard breast MRI improved significantly breast MRI Sensitity (78 to 91 %) without reduction in specificity; Sp was anyway initially high in our study. Nevertheless, we didn t demonstrate a statistical correlation with fibrosis, vascular grading or necrosis with MRE parameters, to explain visco-elastic properties of breast tumours. In conclusion, MR-elastography may be useful to improve breast MRI accuracy. In future studies, MRE sequence may be optimized to allow a bilateral acquisition on both breasts, which would be useful in clinical practice. Future works could include higher number of patients to confirm our results.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

An Image Based Vibration Sensor for Soft Tissue Modal Analysis in a Digital Image Elasto Tomography (DIET) System

Digital Image Elasto Tomography (DIET) is a non-invasive elastographic breast cancer screening technology, relying on image-based measurement of surface vibrations induced on a breast by mechanical actuation. Knowledge of frequency response characteristics of a breast prior to imaging is critical to maximize the imaging signal and diagnostic capability of the system. A non-invasive image based modal analysis system that is designed to be able to robustly and rapidly identify resonant frequencies in soft tissue is presented in this thesis. A feasibility analysis reveals that three images per oscillation cycle are sufficient to capture the relative motion behavior at a given frequency. Moreover, the analysis suggests that 2D motion analysis is able to give an accurate estimation of the response at a particular frequency. Thus, a sweep over critical frequency ranges can be performed prior to imaging to determine critical imaging settings of the DIET system to maximize diagnositc performance. Based on feasibility simulations, a modal analysis system is presented that is based on the existing DIET digital imaging system. A frequency spectrum plot that comprises responses gathered from more than 30 different frequencies can be obtained in about 6 minutes. Preliminary results obtained from both phantom and human trials indicate that distinctive resonant frequencies can be obtained with the modal analysis system. Due to inhomogeneous properties of human breast tissues, different imaging location appear to pick up different resonances. However, there has been very limited clinical data for validating such behavior. Overall, a modal analysis system for soft tissue has been developed in this thesis. The system was first evaluated in simulation, then implemented in hardware and software, and finally successfully validated in silicone phantoms as well as human breasts

Viscoelasticity Imaging of Biological Tissues and Single Cells Using Shear Wave Propagation

Changes in biomechanical properties of biological soft tissues are often associated with physiological dysfunctions. Since biological soft tissues are hydrated, viscoelasticity is likely suitable to represent its solid-like behavior using elasticity and fluid-like behavior using viscosity. Shear wave elastography is a non-invasive imaging technology invented for clinical applications that has shown promise to characterize various tissue viscoelasticity. It is based on measuring and analyzing velocities and attenuations of propagated shear waves. In this review, principles and technical developments of shear wave elastography for viscoelasticity characterization from organ to cellular levels are presented, and different imaging modalities used to track shear wave propagation are described. At a macroscopic scale, techniques for inducing shear waves using an external mechanical vibration, an acoustic radiation pressure or a Lorentz force are reviewed along with imaging approaches proposed to track shear wave propagation, namely ultrasound, magnetic resonance, optical, and photoacoustic means. Then, approaches for theoretical modeling and tracking of shear waves are detailed. Following it, some examples of applications to characterize the viscoelasticity of various organs are given. At a microscopic scale, a novel cellular shear wave elastography method using an external vibration and optical microscopy is illustrated. Finally, current limitations and future directions in shear wave elastography are presented

Simplification of Mathematical Models for Medical Ultrasound Poroelasticity Imaging

The use of an understanding of mechanical properties of tissues for the purposes of medical diagnosis has been going on since the foundation of the medical field as a science. In recent decades, medical ultrasound elastography techniques have been developed and improved and have helped the medical community improve the state of diagnosis and tracking of various diseases like cancer, and lymphedema. Poroelastography, refers to the extension of ultrasound elastography techniques towards imaging the mechanical properties of tissues that are modeled as poroelastic. Currently, the field of poroelastography is stuck, largely due to the complication in the mathematical models surrounding poroelastic materials. This dissertation focuses on the investigation of the suitability of a simplified equation involving a single saturating exponential (i.e. time constant curve) to describe the local time-dependent strain response of non-homogeneous poroelastic materials placed under creep compression. A new algorithm of measuring how precisely a non-linear equation fits a set of data samples from an experiment, the Resimulation of Noise (RoN) algorithm, was developed and implemented for the time constant curve case. The RoN algorithm was shown to track the precision of the fit in a more intuitive and accurate manner than previously used quality of fit metrics. The RoN algorithm coupled with an in-depth FEM simulation study was conducted to see how well the single exponential time-constant curve fit the localized strain samples of a simulated prismatic phantom with a cylindrical inclusion under different permeability and stiffness contrasts. The study showed that, on average, the single exponential time constant curve was suitable within 10% precision for 90% of the phantom's area so long as a mean-mask filter was applied the localized strain images before attempting the curve-fit. Future work in the field of poroelasticity imaging should center around the use of the single exponential time constant curve. This will require the development of a full understanding of how poroelastic material parameter contrast affects the contrast of the measured time constants. Procedures that will help this endeavor: such as the parallelization of the RoN algorithm as well as the development of novel nonhomogeneous poroelastic phantoms with the aid of 3-D printers are also proposed

Digital Image Elasto-Tomography: Mechanical Property Reconstruction from Surface Measured Displacement Data

Interest in elastographic techniques for soft tissue imaging has grown as relevant research continues to indicate a correlation between tissue histology and mechanical stiffness. Digital Image Elasto-Tomography (DIET) presents a novel method for identifying cancerous lesions via a three-dimensional image of elastic properties. Stiffness reconstruction with DIET takes steady-state motion captured with a digital camera array as the input to an elastic property reconstruction algorithm, where finite element methods allow simulation of phantom motion at a range of internal stiffness distributions. The low cost and high image contrast achievable with a DIET system may be particularly suited to breast cancer screening, where traditional modalities such as mammography have issues with limited sensitivity and patient discomfort. Proof of concept studies performed on simulated data sets confirmed the potential of the DIET technique, leading to the development of an experimental apparatus for surface motion capture from a range of soft tissue approximating phantoms. Error studies performed on experimental data from these phantoms using a limited number of shape and modulus parameters indicated that accurate measurements of surface motion provide sufficient information to identify a stiffness distribution in both homogeneous and heterogeneous cases. The elastic reconstruction performed on simulated and experimental data considered both deterministic and stochastic algorithms, with a combination of the two approaches found to give the most accurate results, for a realistic increase in computational cost. The reconstruction algorithm developed has the ability to successfully resolve a hard spherical inclusion within a soft phantom, and in addition demonstrated promise in reconstructing the correct stiffness distribution when no inclusion is present