Related Experiment Video
Updated: May 10, 2026
Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain
Published on: April 20, 2019
Iterative 3D projection reconstruction of (23) Na data with an (1) H MRI constraint
Christine Gnahm1, Michael Bock, Peter Bachert
1Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Purpose:
To 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 (1) H MR data in an iterative reconstruction.
Methods:
An 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.
Results:
In 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 (23) Na MRI of the human brain revealed an SNR increase of 29 ± 7%. Small anatomical structures were reproduced with a mean contrast loss of 14%, whereas in TV-regularized iterative reconstructions a loss of 66% was found.
Conclusion:
The 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.
