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Abstract #0006

Compressed Sensing Multi-Spectral Imaging of the Post-Operative Spine

Pauline Wong Worters1, Kyunghyun Sung1, Kathryn J. Stevens1, Kevin M. Koch2, Brian A. Hargreaves1

1Stanford University, Stanford, CA, United States; 2ASL, GE Healthcare, Waukesha, WI, United States

Multi-spectral imaging (MSI) methods such as MAVRIC, SEMAC and Hybrid have been developed in recent years to provide distortion-free MRI of tissue around metallic implants. However, acquisition times remain lengthy (5-15 minutes) and limit the achievable spatial resolution in routine clinical use. In this work, we demonstrate the feasibility of using compressed sensing (CS) to reduce acquisition time in a retrospective application to patient data with spinal hardware. Results show that retrospective CS-MSI are the same as or better than the original MSI images. We also show that fully sampled MSI and prospectively undersampled T2-weighted CS-MSI (42% scan time reduction) are comparable in terms of image contrast and quality.