PURPOSE: 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. CONCLUSION: 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.
PURPOSE: 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. CONCLUSION: 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: Martin Georg Zeilinger; Marco Wiesmüller; Christoph Forman; Michaela Schmidt; Camila Munoz; Davide Piccini; Karl-Philipp Kunze; Radhouene Neji; René Michael Botnar; Claudia Prieto; Michael Uder; Matthias May; Wolfgang Wuest Journal: Eur Radiol Date: 2020-12-02 Impact factor: 5.315
Authors: Daniel Paech; Sebastian Regnery; Tanja Platt; Nicolas G R Behl; Nina Weckesser; Paul Windisch; Katerina Deike-Hofmann; Wolfgang Wick; Martin Bendszus; Stefan Rieken; Laila König; Mark E Ladd; Heinz-Peter Schlemmer; Jürgen Debus; Sebastian Adeberg Journal: Front Neurosci Date: 2021-12-01 Impact factor: 4.677
Authors: Sebastian Regnery; Nicolas G R Behl; Tanja Platt; Nina Weinfurtner; Paul Windisch; Katerina Deike-Hofmann; Felix Sahm; Martin Bendszus; Jürgen Debus; Mark E Ladd; Heinz-Peter Schlemmer; Stefan Rieken; Sebastian Adeberg; Daniel Paech Journal: Neuroimage Clin Date: 2020-09-12 Impact factor: 4.881