Nonlinear characterization of breast cancer using multi-compression 3D ultrasound elastography in vivo

Abstract

The main objective of this article is to introduce a new nonlinear elastography based classification method for human breast masses. Multi-compression elastography imaging is elucidated in this study to differentiate malignant from benign lesions, based on their nonlinear mechanical behavior under compression. Three classification parameters were used and compared in this work: a new nonlinear parameter based on a power-law behavior of the strain difference between breast masses and healthy tissues, mass-soft tissue strain ratio and the mass relative volume between B-mode and elastography imaging. Using 3D elastography, these parameters were tested in vivo. A pilot study on ten patients was performed, and results were compared with biopsy diagnosis as a gold standard. Initial elastography results showed a good agreement with biopsy outcomes. The new estimated nonlinear parameter had an average value of 0.163 ± 0.063 and 1.642 ± 0.261 for benign and malignant masses, respectively. Strain ratio values for the benign and malignant masses had an average value of 2.135 ± 0.707 and 4.21 ± 2.108, respectively. Relative mass volume was 0.848 ± 0.237 and 2.18 ± 0.522 for benign and malignant masses. In addition to the traditional normal axial strain, new strain types were used for elastography and constructed in 3D, including the first principal, maximum shear and Von Mises strains. The new strains provided an enhanced distinction of the stiff lesion from the soft tissue. In summary, the proposed elastographic techniques can be used as a noninvasive quantitative characterization tool for breast cancer, with the capability of visualizing and separating the masses in a three dimensional space. This may reduce the number of unnecessary painful breast biopsies.

An analysis of the segmentation threshold used in axial–shear strain elastography

“…These findings were independently corroborated at least by one other group (Xu et al 2010, Varghese 2011). The typical appearance of the axial-shear strain pattern due to a stiff inclusion has however been corroborated by several other groups as well (Chen et al 2010, Garcia et al 2009, Sayed et al 2013, Maurice et al 2013). …”

Automated Computed Tomography–Ultrasound Cross-Modality 3-D Contouring Algorithm for Prostate

“…US contrast imaging, with contrast agents loaded with biological tracers, could potentially provide high imaging quality combined with biological information, customized on the target of the tracer. Several other advanced US imaging techniques are already available that could provide additional anatomic and functional information, like contrast diffusion (Kuenen et al 2011), harmonic imaging (Choudhry et al 2000), vibro-acoustography (Alizad et al 2012), elastography (Sayed et al 2013) and tissue typing (Feleppa 2008). Moreover, the combination of CT and US imaging modalities fits well in the RT workflow, where a CT scan is always necessary, as a source of the density information for dose calculation.…”

Strain estimation by a Fourier Series-based extrema tracking algorithm for elastography

“…Over the past two decades, ultrasonic elastography has been recognized as an effective and noninvasive means for detecting the abnormal changes in soft tissues such as breast [1,2,3], liver [4,5,6], prostate [7] and thyroid [8]. For these diseases, normal and abnormal tissues are associated with different stiffness and elasticity.…”