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Abstract #3080

Noise Reduction in Slice Encoding for Metal Artifact Correction Using Singular Value Decomposition

Wenmiao Lu1, Kim Butts Pauly2, Garry Evan Gold2, John Mark Pauly3, Brian Andrew Hargreaves2

1Electrical & Electronic Engr., Nanyang Tech. University, Singapore, Singapore; 2Radiology, Stanford University, Stanford, CA, United States; 3Electrical Engr., Stanford University, Stanford, CA, United States

To obtain distortion-free MR images near metallic implants, SEMAC (slice encoding for metal artifact correction) resolves metal artifacts with additional z-phase encoding, and corrects metal artifacts by combining multiple SEMAC-encoded slices. However, many of the resolved voxels contain only noise rather than signals, which degrades signal-to-noise ratio (SNR) in the corrected images. Here the SEMAC reconstruction is modified to perform denoising using singular value decomposition, which exploits the redundancy in the SEMAC-encoded data received from multiple coils. We demonstrate the efficacy of the proposed technique in several important imaging scenarios where SEMAC-corrected images are liable to relatively low SNR.