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Patient-specific three-dimensional aortic arch modeling for automatic measurements: clinical validation in aortic coarctation

Authors :
Dime Vitanovski
Giacomo Pongiglione
Aurelio Secinaro
Benedetta Leonardi
Giuseppe D'Avenio
Francesco Romeo
Mauro Grigioni
Marco A Perrone
Allen D. Everett
Source :
Journal of Cardiovascular Medicine. 21:517-528
Publication Year :
2020
Publisher :
Ovid Technologies (Wolters Kluwer Health), 2020.

Abstract

AIM A validated algorithm for automatic aortic arch measurements in aortic coarctation (CoA) patients could standardize procedures for clinical planning. METHODS The model-based assessment of the aortic arch anatomy consisted of three steps: first, machine-learning-based algorithms were trained on 212 three-dimensional magnetic resonance (MR) data to automatically allocate the aortic arch position in patients and segment the aortic arch; second, for each CoA patient (N = 33), the min/max aortic arch diameters were measured using the proposed software, manually and automatically, from noncontrast-enhanced three-dimensional steady-state free precession MRI sequence at five selected sites and compared ('internal comparison' referring to the same environment); third, moreover, the same min/max aortic arch diameters were compared, obtaining them independently, manually from common MR management software (MR Viewforum) and automatically from the model (external comparison). The measured sites were: aortic sinus, sino-tubular junction, mid-ascending aorta, transverse arch and thoracoabdominal aorta at the level of the diaphragm. RESULTS Manual and software-assisted measurements showed a good agreement: the difference between diameter measurements was not statistically significant (at α = 0.05), with only one exception, for both internal and external comparison. A high coefficient of correlation was attained for both maximum and minimum diameters in each site (for internal comparison, R > 0.73 for every site, with P

Details

ISSN :
15582035 and 15582027
Volume :
21
Database :
OpenAIRE
Journal :
Journal of Cardiovascular Medicine
Accession number :
edsair.doi.dedup.....5976c39e7b1420182de177f822b1d1ad