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Abstract #3491

Modeling Strain-Encoded (SENC) MRI for Use in Clinical Breast Imaging

Ahmed Amr Harouni1, Nael F. Osman2, Michael A. Jacobs3

1Electrical & Computer Engineering, Johns Hopkins University, Baltimore , MD, United States; 2Department of Radiology, Johns Hopkins University, Baltimore, MD, United States; 3Department of Radiology & Oncology, Johns Hopkins University school of Medicine, Baltimore, MD, United States

Previously, we proposed using mass stiffness to increase specificity of breast cancer detection using MRI. We used strain-Encoded (SENC) MRI to measure strain, which is inversely proportional to stiffness. However, since SENC was originally developed for cardiac applications, 30% compression was used. In this work, we investigate the minimum compression required to apply in order to detect and classify breast masses through finite element method simulations and phantom experiments. Our results, shows that we can detect masses with low compressions (5-10%), but in order to classify benign from malignant masses we need to use higher compression (10-15%).