Slice encoding for metal artifact correction with noise reduction.

Magnetic resonance imaging (MRI) near metallic implants is often hampered by severe metal artifacts. To obtain distortion-free MR images near metallic implants, SEMAC (Slice Encoding for Metal Artifact Correction) corrects metal artifacts via robust encoding of excited slices against metal-induced field inhomogeneities, followed by combining the data resolved from multiple SEMAC-encoded slices. However, as many of the resolved data elements only contain noise, SEMAC-corrected images can suffer from relatively low signal-to-noise ratio. Improving the signal-to-noise ratio of SEMAC-corrected images is essential to enable SEMAC in routine clinical studies. In this work, a new reconstruction procedure is proposed to reduce noise in SEMAC-corrected images. A singular value decomposition denoising step is first applied to suppress quadrature noise in multi-coil SEMAC-encoded slices. Subsequently, the singular value decomposition-denoised data are selectively included in the correction of through-plane distortions. The experimental results demonstrate that the proposed reconstruction procedure significantly improves the SNR without compromising the correction of metal artifacts.