Image Denoising for Metal MRI Exploiting Sparsity and Low Rank Priors.
10.13104/imri.2016.20.4.215
- Author:
Sangcheon CHOI
1
;
Jun Sik PARK
;
Hahnsung KIM
;
Jaeseok PARK
Author Information
1. Department of Computational Science and Engineering, Yonsei University, Seoul, Korea.
- Publication Type:Original Article
- Keywords:
Slice Encoding for Metal Artifact Correction;
Low rank approximation;
Best linear unbiased estimator;
Image denoising;
Sparsity
- MeSH:
Artifacts;
Least-Squares Analysis;
Magnetic Resonance Imaging*;
Methods;
Noise;
Signal-To-Noise Ratio
- From:Investigative Magnetic Resonance Imaging
2016;20(4):215-223
- CountryRepublic of Korea
- Language:English
-
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.
