3D-Dictionary-Learning-CS Reconstruction of Radial 23 Na-MRI-data

3D-dictionary-learning-CS is applied for the reconstruction of radial ²³ Na-MRI-data. The dictionary used for the sparsifying transform consists of 3D-blocks learnt on the gridding-reconstruction of the data. A K-SVD algorithm is used to learn the dictionary and the corresponding representation, the self-consistency of the actual image and the raw-data is enforced through a conjugate gradient algorithm. The performance of the reconstruction algorithm is verified with simulated data (2mm isotropic), phantom ²³ Na-data (1.5mm isotropic) and in-vivo ²³ Na-data (2mm isotropic), showing significant noise reduction compared to the corresponding gridding reconstructions, as well as increased SSIM and reduced RMSE.

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