US strain imaging can facilitate improved classification of benign and malignant breast masses. However, interobserver variability and image quality influence observer performance.

We establish the feasibility of imaging the linear and nonlinear elastic properties of soft tissue using ultrasound. We report results for breast tissue where it is conjectured that these properties may be used to discern malignant tumors from benign tumors. We consider and compare three different quantities that describe nonlinear behavior, including the variation of strain distribution with overall strain, the variation of the secant modulus with overall applied strain and finally the distribution of the nonlinear parameter in a fully nonlinear hyperelastic model of the breast tissue.

Axial strain imaging has been utilized for the characterization of breast masses for over a decade; however, another important feature namely the shear strain distribution around breast masses has only recently been used. In this paper, we examine the feasibility of utilizing in-vivo axial-shear strain imaging for differentiating benign from malignant breast masses. Radiofrequency data was acquired using a VFX 13-5 linear array transducer on 41 patients using a Siemens SONOLINE Antares real-time clinical scanner at the University of Wisconsin Breast Cancer Center. Free-hand palpation using deformations of up to 10% was utilized to generate axial strain and axial-shear strain images using a two-dimensional cross-correlation algorithm from the radiofrequency data loops. Axial-shear strain areas normalized to the lesion size, applied strain and lesion strain contrast was utilized as a feature for differentiating benign from malignant masses. The normalized axial-shear strain area feature estimated on 8 patients with malignant tumors and 33 patients with fibroadenomas was utilized to demonstrate its potential for lesion differentiation. Biopsy results were considered the diagnostic standard for comparison. Our results indicate that the normalized axial-shear strain area is significantly larger for malignant tumors when compared to benign masses such as fibroadenomas. Axial-shear strain pixel values greater than a specified threshold, including only those with correlation coefficient values greater than 0.75, were overlaid on the corresponding B-mode image to aid in diagnosis. A scatter plot of the normalized area feature demonstrates the feasibility of developing a linear classifier to differentiate benign from malignant masses. The area under the receiver operator characteristic curve utilizing the normalized axial-shear strain area feature was 0.996, demonstrating the potential of this feature to noninvasively differentiate between benign and malignant breast masses.

Ultrasonic strain imaging that uses signals from conventional diagnostic ultrasound systems is capable of showing the contrast of tissue elasticity, which provides new diagnostically valuable information. To assess and improve the diagnostic performance of ultrasonic strain imaging, it is essential to have a quantitative measure of image quality. Moreover, it is useful if the image quality measure is simple to interpret and can be used for visual feedback while scanning and as a training tool for operator performance evaluation.This report describes the development of a novel quantitative method for systematic performance assessment that is based on the combination of measures of the accuracy of motion tracking and consistency among consecutive strain fields. The accuracy of motion tracking assesses the reliability of strain images. The consistency among consecutive strain images assesses the signal quality in strain images. The clinical implications of the proposed method to differentiate good or poor strain images are discussed. Results of experiments with tissue-mimicking phantoms and in vivo breasttissue data demonstrate that the performance measure is a useful method for automatically rating elasticity image quality.

Ultrasound strain imaging can differentiate between endometrial polyps and leiomyomas. More data are necessary to validate these results and to ascertain whether other uterine abnormalities can also be differentiated.