A Dictionary Learning Approach for Noise-Robust Image Reconstruction in Low-Field Magnetic Resonance Imaging

Objective: Image denoising has been considered as a separate procedure from image reconstruction which could otherwise be combined with acquisition and reconstruction. This paper discusses a joint image reconstruction and denoising algorithm in low-field MRI using a dictionary learning approach. Method: Our proposed algorithm uses a two-level Bregman iterative method for image reconstruction and image denoising procedure using OMP for sparse coding and SimCO for Dictionary Update and Learning. Results: Experiments were done on a noisy phantom that was obtained from a low field MRI scanner. Results demonstrate that our proposed algorithm performs superior image reconstructions that are almost noise-free. Our proposed method also performed better than the TBMDU algorithm, which performed better than DLMRI, a technique that substantially outperformed other CSMRI based reconstruction methods. However, the TBMDU algorithm is faster than our proposed algorithm due to additional iterations required during the denoising step. Conclusion: An algorithm that jointly performs reconstruction and denoising is essential in medical imaging modalities where image denoising has been a separate process from the reconstruction. Combining the two could save time and could avoid image details to be lost due to having two separate operations. This formulation is essential in imaging modalities like low-field MRI where the image signal is noisy and therefore performing a joint reconstruction and denoising could help improve the quality of the images obtained.

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