3-D visualization and non-linear tissue classification of breast tumors using ultrasound elastography in vivo

Responsive Supramolecular Polymers for Diagnosis and Treatment

Stimuli-responsive supramolecular polymers are ordered nanosized materials that are held together by non-covalent interactions (hydrogen-bonding, metal-ligand coordination, π-stacking and, host-guest interactions) and can reversibly undergo self-assembly. Their non-covalent nature endows supramolecular polymers with the ability to respond to external stimuli (temperature, light, ultrasound, electric/magnetic field) or environmental changes (temperature, pH, redox potential, enzyme activity), making them attractive candidates for a variety of biomedical applications. To date, supramolecular research has largely evolved in the development of smart water-soluble self-assemblies with the aim of mimicking the biological function of natural supramolecular systems. Indeed, there is a wide variety of synthetic biomaterials formulated with responsiveness to control and trigger, or not to trigger, aqueous self-assembly. The design of responsive supramolecular polymers ranges from the use of hydrophobic cores (i.e., benzene-1,3,5-tricarboxamide) to the introduction of macrocyclic hosts (i.e., cyclodextrins). In this review, we summarize the most relevant advances achieved in the design of stimuli-responsive supramolecular systems used to control transport and release of both diagnosis agents and therapeutic drugs in order to prevent, diagnose, and treat human diseases.

Speed of sound and shear wave speed for calf soft tissue composition and nonlinearity assessment

Background

The purpose of this study was threefold: (I) to study the correlation of speed-of-sound (SoS) and shear-wave-speed (SWS) ultrasound (US) in the gastrocnemius muscle, (II) to use reproducible tissue compression to characterize tissue nonlinearity effects, and (III) to compare the potential of SoS and SWS for tissue composition assessment.

Methods

Twenty gastrocnemius muscles of 10 healthy young subjects (age range, 23-34 years, two females and eight males) were prospectively examined with both clinical SWS (GE Logiq E9, in m/s) and a prototype system that measures SoS (in m/s). A reflector was positioned opposite the US probe as a timing reference for SoS, with the muscle in between. Reproducible tissue compression was applied by reducing probe-reflector distance in 5 mm steps. The Ogden hyperelastic model and the acoustoelastic theory were used to characterize SoS and SWS variations with tissue compression and extract novel metrics related to tissue nonlinearity. The body fat percentage (BF%) of the subjects was estimated using bioelectrical impedance analysis.

Results

A weak negative correlation was observed between SWS and SoS (r=-0.28, P=0.002). SWS showed an increasing trend with increasing tissue compression (P=0.10) while SoS values decayed nonlinearly (P<0.001). The acoustoelastic modeling showed a weak correlation for SWS (r=-0.36, P<0.001) but a very strong correlation for SoS (r=0.86, P<0.001), which was used to extract the SoS acoustoelastic parameter. SWS showed higher variability between both calves [intraclass correlation coefficient (ICC) =0.62, P=0.08] than SoS (ICC =0.91, P<0.001). Correlations with BF% were strong and positive for SWS (r=0.60, P<0.001), moderate and negative for SoS (r=-0.43, P=0.05), and moderate positive for SoS acoustoelastic parameter (r=0.48, P=0.03).

Conclusions

SWS and SoS provide independent information about tissue elastic properties. SWS correlated stronger with BF% than SoS, but measurements were less reliable. SoS enabled the extraction of novel metrics related to tissue nonlinearity with potential complementary information.

Breast Tumor Diagnosis Using Finite-Element Modeling Based on Clinical in vivo Elastographic Data

OBJECTIVES

This study exploited finite-element modeling (FEM) to simulate breast tissue multicompression during ultrasound elastography to classify breast tumors based on their nonlinear biomechanical properties.

METHODS

Numeric simulations were first calculated by using 3-dimensional (3D) virtual models with an assumed tumor's geometric dimensions but with actual material properties to test and validate the FEM. Further numeric simulations were used to construct 3D models based on in vivo experimental data to verify our models. The models were designed for each individual in vivo case, emphasizing the geometry, position, and biomechanical properties of the breast tissue. At different compression levels, tissue strains were analyzed between the tumors and the background normal tissues to explore their nonlinearity and classify the tumor type. Tumor classification parameters were deduced by using a power-law relationship between the applied compressive forces and strain differences.

RESULTS

Classification parameters were compared between benign and malignant tumors, for which they were found to be statistically significant in classifying the tumor types (P < .05) by both the validation and verification of FEM. We compared the classification parameters between the in vivo and FEM classifications, for which they were found to be strongly correlated (R = 0.875; P < .001), with no statistical differences between their outcomes (P = .909).

CONCLUSIONS

Good agreement between the model outcomes and the in vivo diagnostics was reported. The implemented models were validated and verified. The introduced 3D modeling method may augment elastographic methods to preliminary classify breast tumors at an early stage.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Introduction of a Measurement Setup to Monitor the Pressure Applied During Handheld Ultrasound Elastography

Shear-wave elastography may produce misleadingly high values if too much pressure is applied during the imaging process. However, in clinical routine there is presently no way to monitor the pressure applied during the measurements. In this work we introduce a novel measurement setup which can directly be attached to an ultrasonic imaging transducer and allows observation of the applied pressure in real time. The setup supports free-hand imaging according to the clinical standard. We tested the setup by carrying out shear-wave elastography under varying pressures on ex vivo animal tissue. The values increased with pressure, as was expected. Thus, the setup is a possible solution for measuring applied pressure in real time.

Difference-Frequency Ultrasound Imaging With Non-Linear Contrast

Conventional ultrasound imaging is based on the scattering of sound from inhomogeneities in the density and the speed of sound and is often used in medicine to resolve pathologic compared to normal tissue. Here we demonstrate a difference-frequency ultrasound (dfUS) imaging method that is based on the interaction of two sound pulses that propagate non-collinearly and intersect in space and time. The dfUS signal arises primarily from the second-order non-linear coefficient, a contrast mechanism that differs from linear and harmonic US imaging. The distinct contrast mechanism allows dfUS to image anatomic features that are not identifiable in conventional US images of salmon and pig kidney tissue. Further, dfUS produces enhanced contrast of glioblastoma tumor implanted in the mouse brain, revealing its potential for improving medical diagnosis. Progress towards a real-time system is discussed.

Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging

We present a 3D extension of the Autoprogressive Method (AutoP) for quantitative quasi-static ultrasonic elastography (QUSE) based on sparse sampling of force-displacement measurements. Compared to current model-based inverse methods, our approach requires neither geometric nor constitutive model assumptions. We build upon our previous report for 2D QUSE and demonstrate the feasibility of recovering the 3D linear-elastic material property distribution of gelatin phantoms under compressive loads. Measurements of boundary geometry, applied surface forces, and axial displacements enter into AutoP where a Cartesian neural network constitutive model (CaNNCM) interacts with finite element analyses to learn physically consistent material properties with no prior constitutive model assumption. We introduce a new regularization term uniquely suited to AutoP that improves the ability of CaNNCMs to extract information about spatial stress distributions from measurement data. Results of our study demonstrate that acquiring multiple sets of force-displacement measurements by moving the US probe to different locations on the phantom surface not only provides AutoP with the necessary information for a CaNNCM to learn the 3D material property distribution, but may significantly improve the accuracy of the Young's modulus estimates. Furthermore, we investigate the trade-offs of decreasing the contact area between the US transducer and phantom surface in an effort to increase sensitivity to surface force variations without additional instrumentation. Each of these modifications improves the ability of CaNNCMs trained in AutoP to learn the spatial distribution of Young's modulus from force-displacement measurements.

Large-Strain 3-D in Vivo Breast Ultrasound Strain Elastography Using a Multi-compression Strategy and a Whole-Breast Scanning System

Non-linear mechanical properties of breast tissue can be employed to diagnose and differentiate breast tumors. To obtain such non-linear mechanical properties, it is necessary to track tissue motion under large deformation. In this study, a multi-compression strategy was utilized to produce large tissue deformation, and a method to estimate 3-D motion of tissue under large deformation was introduced. Given multiple volumes of ultrasound data, the proposed method first estimates volume-to-volume incremental displacements using a 3-D region-growing motion-tracking method. Then, possible outliers among all incremental displacements are removed to avoid error accumulation. Once large displacement errors have been removed, all incremental displacements are registered together to obtain accumulated displacements under large tissue deformation (e.g., >10%). The proposed method was tested with one set of in vivo tumor-bearing ultrasound data acquired from a human subject. A total of 10 small-strain deformation steps were performed to obtain the final accumulated displacement field, in which the breast lesion and its surrounding were deformed by approximately 6% and 16%, respectively. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the elasticity images obtained with the proposed method were all higher than those obtained with a 2-D tracking method. Furthermore, in three orthogonal views of accumulated axial strain images, the breast lesion was clearly visible with good correspondence between the axial strain and B-mode images.

3D Quasi-Static Ultrasound Elastography With Plane Wave In Vivo

In biological tissue, an increase in elasticity is often a marker of abnormalities. Techniques such as quasi-static ultrasound elastography have been developed to assess the strain distribution in soft tissues in two dimensions using a quasi-static compression. However, as abnormalities can exhibit very heterogeneous shapes, a three dimensional approach would be necessary to accurately measure their volume and remove operator dependency. Acquisition of volumes at high rates is also critical to performing real-time imaging with a simple freehand compression. In this study, we developed for the first time a 3D quasi-static ultrasound elastography method with plane waves that estimates axial strain distribution in vivo in entire volumes at high volume rate. Acquisitions were performed with a 2D matrix array probe of 2.5 MHz frequency and 256 elements. Plane waves were emitted at a volume rate of 100 volumes/s during a continuous motorized and freehand compression. 3D B-mode volumes and 3D cumulative axial strain volumes were successfully estimated in inclusion phantoms and in ex vivo canine liver before and after a high intensity focused ultrasound ablation. We also demonstrated the in vivo feasibility of the method using freehand compression on the calf muscle of a human volunteer and were able to retrieve 3D axial strain volume at a high volume rate depicting the differences in stiffness of the two muscles which compose the calf muscle. 3D ultrasound quasi-static elastography with plane waves could become an important technique for the imaging of the elasticity in human bodies in three dimensions using simple freehand scanning.

Recent technological advancements in breast ultrasound

Ultrasound is becoming increasingly common as an imaging tool for the detection and characterization of breast tumors. This paper provides an overview of recent technological advancements, especially those that may have an impact in clinical applications in the field of breast ultrasound in the near future. These advancements include close to 100% fractional bandwidth high frequency (5-18MHz) 2D and 3D arrays, automated breast imaging systems to minimize the operator dependence and advanced processing techniques, such as those used for detection of microcalcifications. In addition, elastography and contrast-enhanced ultrasound examinations that are expected to further enhance the clinical importance of ultrasound based breast tumor screening are briefly reviewed. These techniques have shown initial promise in clinical trials and may translate to more comprehensive clinical adoption in the future.