A Robust Alternative to Regularization in Parallel Imaging Reconstruction Export

@inproceedings{Haeberlin2006, abstract = {An alternative to empirical regularization is proposed for iterative reconstruction from parallel-imaging data, which is based on previously proposed weighted least-squares optimization. It is shown that the choice of the involved weighting parameter is highly robust in practical situations. The parameter choice is critical only in badly conditioned situations.}, author = {Barmet, Christoph and Haeberlin, Maximilian and Pruessmann, Klaas P.}, citeulike-article-id = {3199580}, journal = {Proceedings of the International Society for Magnetic Resonance in Medicine}, keywords = {2006, ismrm, parallel-imaging, reconstruction}, posted-at = {2008-09-06 12:54:28}, priority = {0}, title = {A Robust Alternative to Regularization in Parallel Imaging Reconstruction}, year = {2006} }

TY - CONF ID - Haeberlin2006 L3 - citeulike-article-id:3199580 N2 - An alternative to empirical regularization is proposed for iterative reconstruction from parallel-imaging data, which is based on previously proposed weighted least-squares optimization. It is shown that the choice of the involved weighting parameter is highly robust in practical situations. The parameter choice is critical only in badly conditioned situations. JF - Proceedings of the International Society for Magnetic Resonance in Medicine TI - A Robust Alternative to Regularization in Parallel Imaging Reconstruction KW - 2006 KW - ismrm KW - parallel-imaging KW - reconstruction AU - Barmet, Christoph AU - Haeberlin, Maximilian AU - Pruessmann, Klaas PY - 2006/// ER -