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In-line Automated Tracking for Ventricular Function With Magnetic Resonance Imaging
Author(s) -
Bo Li,
Yingmin Liu,
Christopher J. Occleshaw,
Brett R. Cowan,
Alistair A. Young
Publication year - 2010
Publication title -
jacc. cardiovascular imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.79
H-Index - 120
eISSN - 1936-878X
pISSN - 1876-7591
DOI - 10.1016/j.jcmg.2010.04.013
Subject(s) - steady state free precession imaging , magnetic resonance imaging , tracking (education) , ventricle , artificial intelligence , computer science , line (geometry) , volume (thermodynamics) , ventricular function , computer vision , nuclear medicine , medicine , physics , mathematics , radiology , psychology , pedagogy , geometry , quantum mechanics
An efficient nonrigid registration algorithm was implemented on the image reconstruction computer to enable in-line automatic tracking of features in steady-state free precession cine images. Four-dimensional left ventricle function analysis was performed with and without use of the in-line automatic tracking result. The method was tested in 30 patients referred for cardiac magnetic resonance imaging for a variety of clinical assessments. The time required for in-line tracking was 10 +/- 2 s per slice using an image reconstructor with dual Advanced Micro Devices single-core Opteron 248 CPUs (2.2 GHz) and 8GB random access memory. The precision of clinical estimates of left ventricular volumes was significantly improved relative to the ground truth research estimates with automatic tracking versus without (6 ml vs. 9 ml in end-diastolic volume; 5 ml vs. 10 ml in end-systolic volume; both p < 0.05). In-line automatic tracking of image features shows promise for facilitating clinical analysis of ventricular function.

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