Iterative 3D Projection Reconstruction of 23Na Data with an 1H MRI Constraint

PurposeTo increase the signal-to-noise ratio (SNR) and to reduce artifacts in non-proton magnetic resonance imaging (MRI) by incorporation of a priori information from H-1 MR data in an iterative reconstruction. MethodsAn iterative reconstruction algorithm for 3D projection reconstruction (3DPR) is presented that combines prior anatomical knowledge and image sparsity under a total variation (TV) constraint. A binary mask (BM) is used as an anatomical constraint to penalize non-zero signal intensities outside the object. The BM&TV method is evaluated in simulations and in MR measurements in volunteers. ResultsIn simulated BM&TV brain data, the artifact level was reduced by 20% while structures were well preserved compared to gridding. SNR maps showed a spatially dependent SNR gain over gridding reconstruction, which was up to 100% for simulated data. Undersampled 3DPR Na-23 MRI of the human brain revealed an SNR increase of 297%. Small anatomical structures were reproduced with a mean contrast loss of 14%, whereas in TV-regularized iterative reconstructions a loss of 66% was found. ConclusionThe BM&TV algorithm allows reconstructing images with increased SNR and reduced artifact level compared to gridding and performs superior to an iterative reconstruction using an unspecific TV constraint only. Magn Reson Med 71:1720-1732, 2014. (c) 2013 Wiley Periodicals, Inc.