PurposeThe purpose of this study is to develop a freehand scan three-dimensional (3D) shear wave elasticity imaging (SWEI) method for characterizing the anisotropy of elastic properties in biological tissues. The motivation behind this work lies in addressing the limitations of traditional two-dimensional (2D) SWEI, which only measures shear wave speeds in a single direction, as well as fulfilling the clinical demand for improved medical imaging.ApproachOur imaging system utilizes a high-definition optical camera to continuously track the ultrasonic transducer, collecting spatial position-angle data of the transducer and corresponding two-dimensional SWEI data. By reconstructing three-dimensional SWEI images using these data, we achieved freehand SWEI.ResultsWe validated the accuracy of 2D SWEI on a standard elastic phantom, and then performed 3D SWEI on the pork tenderloin and the triceps brachii of two volunteers. We obtained shear wave speed of 1.82 to 3.12 m / s in the pork tenderloin, shear wave speed of 1.16 to 2.36 m / s in the triceps brachii of non-exercising volunteers, and shear wave speed of 0.55 to 1.63 m / s in the triceps brachii of exercising volunteers, and the maximum shear wave speed directions were generally aligned with the orientation of muscle fibers.ConclusionsWe proposed a method that can overcome the limitations of 2D-SWEI regarding imaging angle while also extending the imaging angle of 3D-SWEI, which could have significant implications for improving the accuracy and safety of medical diagnoses.
KEYWORDS: Image segmentation, Ultrasonography, Tissues, 3D image processing, Breast cancer, Breast, Diagnostics, Image analysis, Magnetic resonance imaging, Speckle, Signal to noise ratio, Visualization
Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.
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