MR image reconstruction from undersampled data by using the iterative refinement procedure |
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Authors: | Lin He Ti-Chiun Chang Stanley Osher Tong Fang Peter Speier |
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Institution: | 1. RICAM, Altenbergerstrasse 69, 4040 Linz, Austria;2. Siemens Corporate Research, Princeton, NJ 08536, USA;3. UCLA Mathematics Department, Box 951555, Los Angeles, CA 90095-1555, USA;4. Siemens AG Med, Erlangen, Germany |
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Abstract: | Magnetic resonance imaging (MRI) reconstruction from sparsely sampled data has been a difficult problem in medical imaging field. We approach this problem by formulating a cost functional that includes a constraint term that is imposed by the raw measurement data in k-space and the L1 norm of a sparse representation of the reconstructed image. The sparse representation is usually realized by total variational regularization and/or wavelet transform. We have applied the Bregman iteration to minimize this functional to recover finer scales in our recent work. Here we propose nonlinear inverse scale space methods in addition to the iterative refinement procedure. Numerical results from the two methods are presented and it shows that the nonlinear inverse scale space method is a more efficient algorithm than the iterated refinement method. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) |
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