Investig Magn Reson Imaging.  2016 Dec;20(4):215-223. 10.13104/imri.2016.20.4.215.

Image Denoising for Metal MRI Exploiting Sparsity and Low Rank Priors

• Choi S
• Park JS
• Kim H
• Park J

• KMID: 2366403
• DOI: http://doi.org/10.13104/imri.2016.20.4.215

Abstract

PURPOSE
The management of metal-induced field inhomogeneities is one of the major concerns of distortion-free magnetic resonance images near metallic implants. The recently proposed method called "Slice Encoding for Metal Artifact Correction (SEMAC)" is an effective spin echo pulse sequence of magnetic resonance imaging (MRI) near metallic implants. However, as SEMAC uses the noisy resolved data elements, SEMAC images can have a major problem for improving the signal-to-noise ratio (SNR) without compromising the correction of metal artifacts. To address that issue, this paper presents a novel reconstruction technique for providing an improvement of the SNR in SEMAC images without sacrificing the correction of metal artifacts.
MATERIALS AND METHODS
Low-rank approximation in each coil image is first performed to suppress the noise in the slice direction, because the signal is highly correlated between SEMAC-encoded slices. Secondly, SEMAC images are reconstructed by the best linear unbiased estimator (BLUE), also known as Gauss-Markov or weighted least squares. Noise levels and correlation in the receiver channels are considered for the sake of SNR optimization. To this end, since distorted excitation profiles are sparse, l1 minimization performs well in recovering the sparse distorted excitation profiles and the sparse modeling of our approach offers excellent correction of metal-induced distortions.
RESULTS
Three images reconstructed using SEMAC, SEMAC with the conventional two-step noise reduction, and the proposed image denoising for metal MRI exploiting sparsity and low rank approximation algorithm were compared. The proposed algorithm outperformed two methods and produced 119% SNR better than SEMAC and 89% SNR better than SEMAC with the conventional two-step noise reduction.
CONCLUSION
We successfully demonstrated that the proposed, novel algorithm for SEMAC, if compared with conventional de-noising methods, substantially improves SNR and reduces artifacts.

Keyword

Slice Encoding for Metal Artifact Correction; Low rank approximation; Best linear unbiased estimator; Image denoising; Sparsity

MeSH Terms

Artifacts
Least-Squares Analysis
Magnetic Resonance Imaging*
Methods
Noise
Signal-To-Noise Ratio

Figure

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  Fig. 1 Fully described multi-dimensional SEMAC (slice encoding for metal artifact correction) data representation.

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  Fig. 2 (a) Reduced data representation in the c-s dimension, (b) reduced data representation in the z-s dimension, (c) normalized singular values of (a) and (b).

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  Fig. 3 BLUE (best linear unbiased estimator) processing pipeline.

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  Fig. 4 Pre-processing of noise using low-rank approximation. 1st row (no low rank processing): (a) peripheral slice, (b) intermediate, (c) central slice, (d) peripheral slice, 2nd row (c-s low rank processing): (e) peripheral slice, (f) intermediate, (g) central slice, (h) peripheral slice, 3rd row (z-s low rank processing): (i) peripheral slice, (j) intermediate, (k) central slice, (l) peripheral slice.

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  Fig. 5 BLUE (best linear unbiased estimator) processing. 1st row (z-s low-rank): (a) peripheral slice, (b) intermediate, (c) central slice, (d) peripheral slice 2nd row (z-s low-rank + BLUE): (e) peripheral slice, (f) intermediate, (g) central slice, (h) peripheral slice.

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  Fig. 6 Sparse recovery processing: (a) z-s low-rank + BLUE, (b) z-s low-rank + BLUE + OMP, (c) magnitude different image.

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  Fig. 7 Comparison of knee images reconstructed using (a) TSE (turbo spin echo), (b) SEMAC (slice encoding for metal artifact correction), (c) SEMAC (slice encoding for metal artifact correction) with the conventional two-step reduction, and (d) with the proposed algorithm.

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