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 July 28, 2016

Steve McAleavey receives NIH R21 grant

dStephen McAleavey, associate professor of biomedical engineering, has received a $408,368 R21 grant from the National Institutes of Health for his project, "Quantification of Shear Wave Strain Dependence in Breast Tissues.” Many women presently undergo breast biopsy due to lesions detected with x-ray and ultrasound imaging. The great majority of these biopsies are negative, resulting in needless expense and worry. The goal of this project is to improve the power of ultrasound imaging to predict if a breast lesion is benign or malignant, by using a novel, high-resolution technique to non-invasively map the non-linear mechanical properties of breast tissue. These properties are determined by the microstructure of the tissue and show marked differences between benign and malignant tissues. Stephen’s co-investigators on this interdisciplinary project are Marvin Doyley, associate professor of electrical and computer engineering; Linda M. Schiffhauer, associate professor of pathology and laboratory medicine, and Avice O’Connell, professor of imaging sciences. 

Abstract: 

The goal of this proposal is to develop and test a combined quasi-static compression and shear wave ultrasound elastography system to image the linear and nonlinear mechanical properties of breast tissues with high spatial resolution. There is strong evidence that non-linear mechanical properties, e.g. nonlinear shear modulus and strain dependent shear wave velocity, can help to differentiate malignant and benign lesions of the breast. Furthermore, tissues with distinct non-linear mechanical properties can exhibit similar small-strain properties and lack contrast in conventional shear wave elastography. The proposed system addresses this limitation and will exploit non-linear mechanical properties to provide an additional feature for lesion classification. We propose to construct and test a system using a combination of quasi-static strain and ARFI-based shear wave elastography to measure the strain dependent shear wave velocity of breast tissue. A range of static “prestrains” will be applied to the tissue while ARFI-induced shear wave velocity is estimated. By combining these shear wave speed measurements with speckle tracking to estimate local strain, we will measure the strain dependence of shear wave velocity of tissues.  We will validate the system with non-linear tissue mimicking phantoms, and collect pilot data on strain dependence of shear wave velocity in mastectomy and in-vivo breast tissues. Recent state mandates to inform women with dense breasts of the potential insensitivity of mammography have resulted in increased ultrasound screening. This screening finds additional cancers, but also many more false positives.  High resolution imaging of non-linear mechanical properties could improve the positive predictive value of ultrasound and allow more patients to be followed rather than biopsied, leading to significant reductions in health care costs.