MRI artifact correction using sparse + low-rank decomposition of annihilating filter-based hankel matrix
Kyong Hwan Jin1,2, Ji-Yong Um1, Dongwook Lee1
1Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), 373-1 Guseong-Dong Yuseong-Gu, Daejon, 305-701, Republic of Korea.
Purpose:
Magnetic resonance imaging (MRI) artifacts are originated from various sources including instability of an magnetic resonance (MR) system, patient motion, inhomogeneities of gradient fields, and so on. Such MRI artifacts are usually considered as irreversible, so additional artifact-free scan or navigator scan is necessary. To overcome these limitations, this article proposes a novel compressed sensing-based approach for removal of various MRI artifacts.
Theory:
Recently, the annihilating filter based low-rank Hankel matrix approach was proposed. The annihilating filter based low-rank Hankel matrix exploits the duality between the low-rankness of weighted Hankel structured matrix and the sparsity of signal in a transform domain. Because MR artifacts usually appeared as sparse k-space components, the low-rank Hankel matrix from underlying artifact-free k-space data can be exploited to decompose the sparse outliers.
Methods:
The sparse + low-rank decomposition framework using Hankel matrix was proposed for removal of MRI artifacts. Alternating direction method of multipliers algorithm was employed for the minimization of associated cost function with the initialized matrices from a factorization-based matrix completion.
Results:
Experimental results demonstrated that the proposed algorithm can correct MR artifacts including herringbone (crisscross), motion, and zipper artifacts without image distortion.
Conclusion:
The proposed method may be a robust correction solution for various MRI artifacts that can be represented as sparse outliers. Magn Reson Med 78:327-340, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
Insights
This study introduces a new compressed sensing method to remove various magnetic resonance imaging (MRI) artifacts. The technique effectively corrects common MRI artifacts like herringbone, motion, and zipper, improving image quality without distortion.
Area of Science:
- Medical Imaging
- Image Processing
- Biomedical Engineering
Background:
- Magnetic Resonance Imaging (MRI) artifacts arise from system instability, patient motion, and field inhomogeneities.
- Current methods for MRI artifact correction often require additional scans, increasing time and cost.
- Artifacts are typically considered irreversible, necessitating complex workarounds.
Purpose of the Study:
- To propose a novel compressed sensing-based approach for the removal of various MRI artifacts.
- To develop a method that overcomes the limitations of existing artifact correction techniques.
- To provide a robust solution for improving the quality of MRI scans.
Main Methods:
- Utilized a sparse + low-rank decomposition framework employing a Hankel matrix.
- Leveraged the annihilating filter based low-rank Hankel matrix approach.
- Employed the alternating direction method of multipliers algorithm for cost function minimization.
Main Results:
- The proposed algorithm successfully corrected common MRI artifacts, including herringbone (crisscross), motion, and zipper artifacts.
- Experimental results demonstrated artifact correction without introducing image distortion.
- The method proved effective in identifying and removing sparse outliers representing artifacts.
Conclusions:
- The developed method offers a robust solution for correcting diverse MRI artifacts.
- The approach is particularly effective for artifacts representable as sparse outliers in k-space.
- This technique enhances the reliability and diagnostic value of MRI scans.
Related Concept Videos
01:14NMR Spectrometers: Resolution and Error Correction
01:24Magnetic Resonance Imaging