Literature DB >> 18347027

Left ventricular ejection fraction using 64-slice CT coronary angiography and new evaluation software: initial experience.

M S Krishnam1, A Tomasian, Michael Iv, S G Ruehm, R Saleh, C Panknin, J G Goldin.   

Abstract

The purpose of this study was to evaluate the feasibility and reliability of software-based quantification of left ventricular function using 64-slice CT coronary angiography. Data were collected from 26 subjects who underwent a 64-slice coronary CT angiography study. Two volumetric data sets at end diastole and end systole were reconstructed from each scan by means of retrospective electrocardiogram gating. Data sets were evaluated with a prototype of now commercially available software (Syngo Circulation I; Siemens Medical Solutions, Erlangen, Germany), which automatically segments the blood volume in the left ventricle after the user defines the mitral valve plane and any point within the ventricle. After segmentation of the blood pool in end systole and end diastole, the software automatically measures end systolic and end diastolic volume and calculates stroke volume and ejection fraction (EF). Two readers processed all CT data sets twice to assess for intra- and inter-observer variation. In addition, CT EF measurements were compared with those obtained by clinical echocardiography. Intra-observer variation for the calculated EF with CT were 13.6% and 15.6% for Readers 1 and 2, respectively. No significant difference in left ventricular functional parameters on CT existed between the readers (p > 0.05). A Bland-Altman plot revealed a slight mean difference between EF measurements on CT and echocardiography, with all differences falling within two standard deviations of the mean in the setting of wide limits of agreement. In conclusion, assessment of left ventricular EF from CT coronary data using the new analysis software is rapid and easy. The software is user-friendly and provides good reproducibility for EF measurements with CT.

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Year:  2008        PMID: 18347027     DOI: 10.1259/bjr/54748900

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  7 in total

1.  Automatic vs semi-automatic global cardiac function assessment using 64-row CT.

Authors:  J Greupner; E Zimmermann; B Hamm; M Dewey
Journal:  Br J Radiol       Date:  2011-11-01       Impact factor: 3.039

2.  Functional parameters of the left ventricle: comparison of cardiac MRI and cardiac CT in a large population.

Authors:  A Palumbo; E Maffei; C Martini; G Messalli; S Seitun; R Malagò; A Aldrovandi; E Emiliano; A Cuttone; A Weustink; N Mollet; F Cademartiri
Journal:  Radiol Med       Date:  2010-02-22       Impact factor: 3.469

Review 3.  Comparison of Echocardiography, Cardiac Magnetic Resonance, and Computed Tomographic Imaging for the Evaluation of Left Ventricular Myocardial Function: Part 1 (Global Assessment).

Authors:  Menhel Kinno; Prashant Nagpal; Stephen Horgan; Alfonso H Waller
Journal:  Curr Cardiol Rep       Date:  2017-01       Impact factor: 2.931

4.  Left and right ventricle assessment with Cardiac CT: validation study vs. Cardiac MR.

Authors:  Erica Maffei; Giancarlo Messalli; Chiara Martini; Koen Nieman; Onofrio Catalano; Alexia Rossi; Sara Seitun; Andrea I Guaricci; Carlo Tedeschi; Nico R Mollet; Filippo Cademartiri
Journal:  Eur Radiol       Date:  2012-01-24       Impact factor: 5.315

5.  Comparison of (semi-)automatic and manually adjusted measurements of left ventricular function in dual source computed tomography using three different software tools.

Authors:  G J de Jonge; P M A van Ooijen; J Overbosch; A Litcheva Gueorguieva; M C Janssen-van der Weide; M Oudkerk
Journal:  Int J Cardiovasc Imaging       Date:  2010-10-23       Impact factor: 2.357

6.  MDCT Derived Left Ventricular Function in Relation to Echocardiography: Validation and Revising the Role with the Evolving Technology.

Authors:  Shilpa Hegde; Venkatraman Bhat; Karthik Gadabanahalli; Murugan Kuppuswamy
Journal:  J Cardiovasc Echogr       Date:  2014 Jan-Mar

7.  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

  7 in total

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