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Abstract #1524

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

Nicolas G.R. Behl 1 , Christine Gnahm 1 , Peter Bachert 1 , and Armin M. Nagel 1

1 Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

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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