A MR image sparse reconstruction method based on compressed sensing

Nan Sun; Qi Dai II

doi:10.1117/12.2282083

21 July 2017 .

Nan Sun, Qi Dai II

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104203Z (2017) https://doi.org/10.1117/12.2282083
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

Abstract

Aiming at solving the defect, slow speed of magnetic resonance imaging (MRI), we propose a magnetic resonance image reconstruction method based on compressed sensing (CS), combining Shift-Invariant Discrete Wavelet Transformation (SIDWT) and dictionary learning (DL). Since the method is based on CS, it obtains the part of k-space image data by undersampling and reconstructs the original image by SIDWT. Then the dictionary learning and sparse coding phase are used in the sparse representation of the original image. Finally, the undersampling image is reconstructed by image iteration update stage, and the quality of the reconstructed image is evaluated objectively via the Peak Signal-to-Noise Ratio (PSNR). Compared with the existing sparse reconstruction algorithm, the proposed method achieves high quality reconstruction of undersampled MR images, and shortens the time of MR image signal acquisition.

Citation Download Citation

Nan Sun and Qi Dai II ".", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104203Z (21 July 2017); https://doi.org/10.1117/12.2282083

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