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Automated liver stiffness measurements with magnetic resonance elastography
Author(s) -
Dzyubak Bogdan,
Glaser Kevin,
Yin Meng,
Talwalkar Jayant,
Chen Jun,
Manduca Armando,
Ehman Richard L.
Publication year - 2013
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.23980
Subject(s) - intraclass correlation , magnetic resonance elastography , stiffness , elastography , correlation coefficient , magnetic resonance imaging , confidence interval , correlation , computer science , homogeneous , biomedical engineering , nuclear medicine , artificial intelligence , mathematics , medicine , ultrasound , materials science , radiology , statistics , machine learning , reproducibility , geometry , combinatorics , composite material
Purpose To provide a fully automated algorithm for obtaining stiffness measurements from hepatic magnetic resonance elastography (MRE) images that are consistent with measurements performed by expert readers. Materials and Methods An initial liver contour was found using an adaptive threshold and expanded using an active contour to select a homogeneous area of the liver. The confidence map generated during the stiffness calculation was used to select a region of reliable wave propagation. The average stiffness within the automatically generated region of interest (ROI) was compared to measurements by two trained readers in a set of 88 clinical test cases ranging from healthy to severely fibrotic. Results The stiffness measurements reported by the readers differed by −6.76% ± 22.8% (95% confidence) and had an intraclass correlation coefficient (ICC) of 0.972 ( P < 0.05). The algorithm and the more experienced reader differed by 4.32% ± 14.9 with an ICC of 0.987. Conclusion The automated algorithm performed reliably, even though MRE acquisitions often have motion artifacts present. The correlation between the automated measurements and those from the trained readers was superior to the correlation between the readers. J. Magn. Reson. Imaging 2013;38:371–379. © 2013 Wiley Periodicals, Inc.