Slice encoding for metal artifact correction with noise reduction

Abstract

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.

Figure 1

(a) Schematic diagram of SEMAC imaging sequence, which extends a VAT spin echo sequence with additional z-phase encoding. (b) Illustration of SEMAC working principle. The additional z-phase encoding resolves the distorted excitation profiles of SEMAC-encoded slices (highlighted in different colors). The reconstruction procedure of SEMAC assigns the resolved data back to their actual voxel locations, and corrects the through-plane distortions by combining multiple SEMAC-encoded slices.

Figure 2

Phantom image comparisons between (a) SEMAC with 2× ARC acceleration and 60% partial Fourier (ky) acquisition, and their counterparts (b, c) obtained from the sumof-squares combination and the proposed technique for noise reduction. It can be seen that both the sum-of-squares combination and the proposed reconstruction procedure significantly improved the SNR. To quantify the SNR improvement, a uniform foreground region was selected in the corrected images (highlighted with dashed box).

Figure 3

Comparison of the SEMAC-corrected images of a neck study. This neck study was performed with accelerated SEMAC acquisition. The image in (a) was obtained from the existing post-processing procedure of SEMAC. The image in (b) was obtained by combining the resolved data with a sum-of-squares, which have superior SNR, but suffer from imperfect correction of signal voids near the metallic implant (arrows). In comparison, the image in (c) was obtained by applying the SVD-based denoising to remove quadrature noise. The image in (d) was obtained from the proposed post-processing procedure, which consist of both the SVD-based denoising and the selective data inclusion.

Figure 4

Comparison of the SEMAC-corrected images of a pelvis study. This pelvis study was performed with accelerated SEMAC acquisition with STIR. The image in (a) was obtained from the existing SEMAC reconstruction procedure, which combines all resolved data with a complex sum. The image in (b) was obtained by combining all resolved data with a sum-of-squares. In this case, the sum-of-squares produces superior SNR, but causes two undesired side effects, namely altered image contrast due to the reduced dynamic range and the ”ripple artifact” near the implant (black arrow). In comparison, the image in (c) was obtained by applying the SVD-based denoising to remove the quadrature noise. The image in (d) was obtained from the proposed reconstruction procedure, which consist of both the SVD-based denoising and the selective data inclusion. The proposed reconstruction procedure markedly improves the SNR of the STIR SEMAC image and preserves the unambiguous identification of fluid near the implant (white arrow).