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Estimating 3D Ventricular Shape From 2D Echocardiography: Feasibility and Effect of Noise

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 10263)


Many cardiac diseases are associated with changes in ventricular shape. However, in daily practice, the heart is mostly assessed by 2D echocardiography only. While 3D techniques are available, they are rarely used. In this paper we analyze to which extent it is possible to obtain the 3D shape of a left ventricle (LV) using measurements from 2D echocardiography. First, we investigate this using synthetic datasets, and afterwards, we illustrate it in clinical 2D echocardiography measurements with corresponding 3D meshes obtained using 3D echocardiography. We demonstrate that standard measurements taken in 2D allow quantifying only the ellipsoidal shape of the ventricle, and that capturing other shape features require either additional geometrical measurements or clinical information related to shape remodelling. We show that noise in the measurements is the primary cause for poor association between the measurements and the LV shape features and that an estimated \(10\%\) level of noise on the 2D measurements limits the recoverability of shape. Finally we show that clinical variables relating to the clinical history can substitute the lack of geometric measurements, thus providing alternatives for shape assessment in daily practice.


  • LV shape
  • LV measurements
  • Echocardiography
  • Shape prediction
  • LV measurement accuracy

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This study was partially supported by the Spanish Ministry of Economy and Competitiveness (grant TIN2014-52923-R; Maria de Maeztu Units of Excellence Programme - MDM-2015-0502), FEDER and the European Union Horizon 2020 Programme for Research and Innovation, under grant agreement No. 642676 (CardioFunXion).

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Correspondence to Gabriel Bernardino .

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Bernardino, G. et al. (2017). Estimating 3D Ventricular Shape From 2D Echocardiography: Feasibility and Effect of Noise. In: Pop, M., Wright, G. (eds) Functional Imaging and Modelling of the Heart. FIMH 2017. Lecture Notes in Computer Science(), vol 10263. Springer, Cham.

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  • Print ISBN: 978-3-319-59447-7

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