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Automated estimation of regional mean transition times and radial velocities from cine magnetic resonance images: Evaluation in normal subjects
Author(s) -
ElBerbari Racha,
Kachenoura Nadjia,
Redheuil Alban,
Giron Alain,
Mousseaux Elie,
Herment Alain,
Bloch Isabelle,
Frouin Frédérique
Publication year - 2009
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.21798
Subject(s) - magnetic resonance imaging , segmentation , short axis , automated method , nuclear medicine , physics , computer science , medicine , nuclear magnetic resonance , mathematics , artificial intelligence , long axis , geometry , radiology
Purpose To assess regional ventricular function via an accurate and automated definition of functional parameters. Materials and Methods An automated method is proposed that estimates reliable regional normalized mean transition times ( F mc ) and mean radial velocities ( V m ) from cine images. This approach combines a quantitative parametric imaging method and an automated detection of the endocardial border, which is robust to the presence of papillary muscles and nonhomogeneities within the left ventricular cavity. Steady‐state free‐precession cine‐magnetic resonance imaging (MRI) of 36 healthy subjects was analyzed. Results The quality of the automated segmentation was assessed on a subgroup of 20 subjects by comparing the results with the manual contours traced by an expert. The comparison of functional parameters estimated consequently using the automated and the manual contours yielded ( y = 0.959 x + 0.016, R = 0.964) for F mc and ( y = 0.883 x + 0.505, R = 0.935) for V m . On the entire group, F mc was equal to 0.392 ± 0.069 and V m to 5.4 ± 2.3 cm/s. Increasing values of the temporal parameter from the apex to the base and larger values in the septal wall than in lateral wall were demonstrated and were in accordance with the physiology. Conclusion The proposed method ensures an automated and robust assessment of regional wall motion parameters, which could be clinically useful. J. Magn. Reson. Imaging 2009;30:236–242. © 2009 Wiley‐Liss, Inc.