Literature DB >> 19540692

Quantitative right and left ventricular functional analysis during gated whole-chest MDCT: a feasibility study comparing automatic segmentation to semi-manual contouring.

Emmanuel Coche1, Matthew J Walker, Francis Zech, Rodolphe de Crombrugghe, Alain Vlassenbroek.   

Abstract

PURPOSE: To evaluate the feasibility of an automatic, whole-heart segmentation algorithm for measuring global heart function from gated, whole-chest MDCT images.
MATERIAL AND METHODS: 15 patients with suspicion of PE underwent whole-chest contrast-enhanced MDCT with retrospective ECG synchronization. Two observers computed right and left ventricular functional indices using a semi-manual and an automatic whole-heart segmentation algorithm. The two techniques were compared using Bland-Altman analysis and paired Student's t-test. Measurement reproducibility was calculated using intraclass correlation coefficient.
RESULTS: Ventricular analysis with automatic segmentation was successful in 13/15 (86%) and in 15/15 (100%) patients for the right ventricle and left ventricle, respectively. Reproducibility of measurements for both ventricles was perfect (ICC: 1.00) and very good for automatic and semi-manual measurements, respectively. Ventricular volumes and functional indices except right ventricular ejection fraction obtained from the automatic method were significantly higher for the RV compared to the semi-manual methods.
CONCLUSIONS: The automatic, whole-heart segmentation algorithm enabled highly reproducible global heart function to be rapidly obtained in patients undergoing gated whole-chest MDCT for assessment of acute chest pain with suspicion of pulmonary embolism. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

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Year:  2009        PMID: 19540692     DOI: 10.1016/j.ejrad.2009.05.037

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  2 in total

1.  ECG-gated computed tomography to assess pulmonary capillary wedge pressure in pulmonary hypertension.

Authors:  Nancy Sauvage; Emilie Reymond; Adrien Jankowski; Marion Prieur; Christophe Pison; Hélène Bouvaist; Gilbert R Ferretti
Journal:  Eur Radiol       Date:  2013-06-09       Impact factor: 5.315

2.  Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning.

Authors:  Hyun Jung Koo; June Goo Lee; Ji Yeon Ko; Gaeun Lee; Joon Won Kang; Young Hak Kim; Dong Hyun Yang
Journal:  Korean J Radiol       Date:  2020-06       Impact factor: 3.500

  2 in total

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