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Publication Detail
Statistical Shape Analysis of the Right Ventricular Outflow Tract in Patients with Tetralogy of Fallot Congenital heart defects and pediatric cardiology applications
  • Publication Type:
    Conference presentation
  • Authors:
    Couvreur S, Nowacka A, Biffi B, Bruse J, Burriesci G, Taylor A, Capelli C, Schievano S
  • Date:
  • Name of Conference:
    8th World Congress of Biomechanics
  • Conference place:
  • Conference start date:
  • Conference finish date:
  • Notes:
Introduction Tetralogy of Fallot (TOF) is the most common congenital heart disease [1] with >80% of children expected to live beyond the age of 40 [2]. However, due to gradual loss of pulmonary valve function after the initial surgical repair, resulting in severe pulmonary regurgitation (PR) [3], TOF patients require frequent surgical revisions. To delay open-heart surgery in TOF, a non-invasive percutaneous pulmonary valve implantation (PPVI) device was first introduced by Bonhoeffer et al. [4] followed by others [5], showing satisfactory clinical performance. However, morphological factors still limit PPVI eligibility. This research focuses on automatically defining the shape of the right ventricular outflow tract (RVOT) in TOF patients with PR late after surgical repair in order to assess the variations of anatomy in this population and guide the design of new PPVI devices. Methods TOF patients (n=59, age 25±15 years) referred to our centre for follow-up cardiovascular magnetic resonance imaging (MRI) between August 2007 and April 2017 were retrospectively selected for this study – inclusion criteria: i) steady-state free precession cardiac-gated 3D whole-heart images; ii) moderate to severe PR (regurgitant fraction (RF) 39±10%); and iii) no metal artefacts. Images were segmented using an automatic multi-atlas propagation algorithm [6]. The RVOT and branch pulmonary arteries (PAs) reconstructed volumes were delimited proximally by the origin of the septomarginal trabeculation (SMT) and distally by the first segmental branching [7]. Registration was performed using an iterative closest point (ICP) algorithm. A statistical shape analysis (SSA) framework [8] was used to calculate the principal component (PC) modes of shape variation in the population from unsupervised statistical learning. PC mode correlation with body surface area (BSA) and RF was analysed. P-values ≤0.05 were considered statistically significant. Results The first three PC modes accounted for 36% of the variance. Mode 1 was most correlated to BSA (r=0.66, p≤0.001) describing size differences, together with mode 2 (r=0.47, p≤0.001). There was no correlation between the following modes and BSA. Mode 2 revealed presence of a highly enlarged pulmonary trunk whilst mode 3 described elongation of the vessel and was indeed correlated with the vessel centreline length (r=0.57, p≤ 0.001). Mode 3 was inversely correlated with pulmonary RF (r=-0.43, p≤ 0.001). Discussion Advanced image processing and statistical shape analysis of the RVOT in TOF can help identify shape features important to guide the design and optimisation of new PPVI devices through finite element simulations, benefitting the largest number of patients requiring a new valve. Acknowledgements EPSRC Grant EP/N02124X/1 References [1] Hunter, PLoS ONE (2013) [2] Moons, Circulation (2010) [3] Frigiola, Circulation (2004) [4] Bonhoeffer, Lancet (2000) [5] Ansari, JACC (2015) [6] Zuluaga, LNCS (2013) [7] Antiga, MBEC (2008) [8] Bruse, BMC MI (2016)
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