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Abstract #1017

Prospective Motion Correction for T2- & Diffusion-Weighted Breast Imaging with FADE

Kristin L. Granlund1,2, Ernesto Staroswiecki1,2, Catherine J. Moran1, Marcus T. Alley1, Bruce L. Daniel1, Brian A. Hargreaves1

1Radiology, Stanford University, Stanford, CA, United States; 2Electrical Engineering, Stanford University, Stanford, CA, United States

Diffusion-weighted imaging is sensitive to all sources of motion and this is a challenge for breast imaging due to cardiac and respiratory motion. FADE is a steady-state sequence that can acquire T2- and diffusion-weighted images. We study the effectiveness of four different methods (breath holding, cardiac gating, respiratory gating, and non-sequential PE ordering) to reduce the motion artifacts in FADE images. Signal variations in the magnitude of projection data and image quality are evaluated. By correcting the motion artifact, we are able to generate high-quality T2- and diffusion-weighted images.