ICASENSE: Sensitivity mapping using Independent Component Analysis for parallel Magnetic Resonance Imaging

Gael Le Bec; Kosai Raoof; Aktham Asfour; J-P Yonnet

doi:10.1109/IEMBS.2005.1615409

ICASENSE: Sensitivity mapping using Independent Component Analysis for parallel Magnetic Resonance Imaging

Conf Proc IEEE Eng Med Biol Soc. 2005:2005:4275-7. doi: 10.1109/IEMBS.2005.1615409.

Authors

Gael Le Bec¹, Kosai Raoof, Aktham Asfour, J-P Yonnet

Affiliation

¹ Laboratory of Images and Signals and the Laboratoire d'Electrotechnique de Grenoble, BP 46, 38402 St Martin d'Heres CEDEX, FRANCE (phone: +33-476-82-71-39; fax: +33-476-82- 63-84; e-mail: lebec@ lis.inpg.fr).

PMID: 17281179
DOI: 10.1109/IEMBS.2005.1615409

Abstract

Parallel Magnetic Resonance Imaging (MRI) methods employ receiver coils sensitivities to reduce imaging time: reconstruction algorithms need RF field maps which must be measured or estimated. Assuming statistical independence of different regions in a MR image, we consider the sensitivity estimation as a Blind Source Separation (BSS) problem that can be solved with Independent Component Analysis (ICA). This new formulation permits sensitivity maps extraction from only one MR acquisition, without calibration step or acquisition of additional k-space lines. Simulation results are presented for sensitivity encoded (SENSE) MR images, proving that sensitivity data can be extracted from statistical properties of the image, using the method ICASENSE.