title: Three-dimensional dictionary-learning reconstruction of (23)Na MRI data.
abstract: OBJECTIVE
  To reduce noise and artifacts in (23)Na 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 (23)Na 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 (23)Na 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 (23)Na measurements in the volunteers that show markedly reduced noise and undersampling artifacts in the case of 3D-DLCS reconstructions.
  
  
  CONCLUSIONS
  The 3D-DLCS algorithm enables precise reconstruction of undersampled (23)Na MRI data with markedly reduced noise and artifact levels compared with NUFFT reconstruction. Small structures are well preserved.
authors.affiliation: Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Nicolas G R
authors.initials: NG
authors.lastname: Behl
authors.affiliation: Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Christine
authors.initials: C
authors.lastname: Gnahm
authors.affiliation: Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Peter
authors.initials: P
authors.lastname: Bachert
authors.affiliation: Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Mark E
authors.initials: ME
authors.lastname: Ladd
authors.affiliation: Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Armin M
authors.initials: AM
authors.lastname: Nagel
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D019047:Phantoms, Imaging; D012975:Sodium Isotopes / Q000008:administration & dosage / Q000737:chemistry* / Q000378:metabolism; D055815:Young Adult
chemical_list: D012975:Sodium Isotopes
keywords: compressed sensing; dictionary learning; iterative reconstruction; nonproton MRI; projection reconstruction; sodium MRI