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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