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Reconstruction of compressively sampled MR images based on a local shrinkage thresholding algorithm with curvelet
Hanlin Wang1,2, Yuxuan Zhou1,3, Xiaoling Wu1,3
1School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211000, China.
Medical & Biological Engineering & Computing
|August 5, 2019
Summary
A new Local Shrinkage Thresholding Algorithm (LSTA) improves Magnetic Resonance Imaging (MRI) reconstruction. This method enhances image quality and reduces blurring by using local thresholds and curvelet transforms for faster, more accurate results.
Area of Science:
- Medical Imaging
- Image Reconstruction
- Signal Processing
Background:
- Iterative Shrinkage Thresholding Algorithm (ISTA) is common for MRI reconstruction but uses inefficient global thresholding.
- Global thresholding limits the efficiency of MRI reconstruction algorithms.
Purpose of the Study:
- To introduce a novel Local Shrinkage Thresholding Algorithm (LSTA) for improved MRI reconstruction.
- To enhance MRI reconstruction efficiency and performance by adapting shrinkage based on image elements.
Main Methods:
- Proposed the Local Shrinkage Thresholding Algorithm (LSTA) utilizing local threshold shrinkage.
- Combined LSTA with curvelet transform (CLSTA) to leverage sparsity for efficiency.
- Compared CLSTA against ISTA, GTIA, and FISTA using human brain and cervical MR images.
Main Results:
- CLSTA demonstrated superior reconstruction performance over ISTA, GTIA, and FISTA.
- Evaluated performance using metrics: Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Mutual Information (NMI), Transferred Edge Information (TEI), and iteration count.
- The proposed method significantly reduced image blurring and preserved detailed information.
Conclusions:
- The CLSTA offers enhanced MRI reconstruction performance compared to existing methods.
- LSTA effectively adjusts shrinkage for individual image elements, improving detail preservation.
- The combination of local shrinkage and curvelet transform presents a promising approach for efficient MRI image reconstruction.
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