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Combined magnitude and phase‐based segmentation of the cerebral cortex in 7T MR images of the elderly
Author(s) -
Doan Nhat Trung,
van Rooden Sanneke,
Versluis Maarten J.,
Webb Andrew G.,
van der Grond Jeroen,
van Buchem Mark A.,
Reiber Johan H.C.,
Milles Julien
Publication year - 2012
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.23623
Subject(s) - segmentation , magnitude (astronomy) , artificial intelligence , computer science , cluster analysis , pattern recognition (psychology) , magnetic resonance imaging , phase (matter) , contrast (vision) , nuclear magnetic resonance , nuclear medicine , physics , medicine , radiology , quantum mechanics , astronomy
Abstract Purpose: To propose a new method that integrates both magnitude and phase information obtained from magnetic resonance (MR) T* 2 ‐weighted scans for cerebral cortex segmentation of the elderly. Materials and Methods: This method makes use of K‐means clustering on magnitude and phase images to compute an initial segmentation, which is further refined by means of transformation with reconstruction criteria. The method was evaluated against the manual segmentation of 7T in vivo MR data of 20 elderly subjects (age = 67.7 ± 10.9). The added value of combining magnitude and phase was also evaluated by comparing the performance of the proposed method with the results obtained when limiting the available data to either magnitude or phase. Results: The proposed method shows good overlap agreement, as quantified by the Dice Index (0.79 ± 0.04), limited bias (average relative volume difference = 2.94%), and reasonable volumetric correlation ( R = 0.555, p = 0.011). Using the combined magnitude and phase information significantly improves the segmentation accuracy compared with using either magnitude or phase. Conclusion: This study suggests that the proposed method is an accurate and robust approach for cerebral cortex segmentation in datasets presenting low gray/white matter contrast. J. Magn. Reson. Imaging 2012;36:99–109. © 2012 Wiley Periodicals Inc.