Three-Dimensional Dictionary-Learning Reconstruction of 23Na MRI Data

Purpose: To reduce noise and artifacts in Na-23 MRI with a Compressed Sensing reconstruction and a learned dictionary as sparsifying transform. Methods: A three-dimensional dictionary-learning compressed sensing reconstruction algorithm (3D-DLCS) for the reconstruction of undersampled 3D radial Na-23 data is presented. The dictionary used as the sparsifying transform is learned with a K-singular-value-decomposition (K-SVD) algorithm. The reconstruction parameters are optimized on simulated data, and the quality of the reconstructions is assessed with peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The performance of the algorithm is evaluated in phantom and in vivo Na-23 MRI data of seven volunteers and compared with nonuniform fast Fourier transform (NUFFT) and other Compressed Sensing reconstructions. Results: The reconstructions of simulated data have maximal PSNR and SSIM for an undersampling factor (USF) of 10 with numbers of averages equal to the USF. For 10-fold undersampling, the PSNR is increased by 5.1 dB compared with the NUFFT reconstruction, and the SSIM by 24%. These results are confirmed by phantom and in vivo Na-23 measurements in the volunteers that show markedly reduced noise and undersampling artifacts in the case of 3D-DLCS reconstructions. Conclusion: The 3D-DLCS algorithm enables precise reconstruction of undersampled Na-23 MRI data with markedly reduced noise and artifact levels compared with NUFFT reconstruction. Small structures are well preserved. (C) 2015 Wiley Periodicals, Inc.