A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta

BMC Medical Imaging

Technical Advance

Kristin McLeod¹  Jan L. Bruse²  Hopewell N. Ntsinjana²  Claudio Capelli²  Tain-Yen Hsia²  Andrew M. Taylor²  Silvia Schievano²  Giovanni Biglino³  Xavier Pennec⁴  Maxime Sermesant⁴ 

[1] Cardiac Modelling Department, Simula Research Laboratory, Oslo, Norway;Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France;Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK;Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK;Bristol Heart Institute, School of Clinical Sciences, University of Bristol, Bristol, UK;Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France;

关键词: Statistical shape model (SSM);    3D Shape analysis;    Coarctation of the aorta;    Congenital heart disease;    Computational modelling;   

DOI  :  10.1186/s12880-016-0142-z

 received in 2015-11-12, accepted in 2016-05-19,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundMedical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements.MethodsSteps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient’s anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters.ResultsThe computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors.ConclusionsThe suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.

【 授权许可】

CC BY   
© The Author(s). 2016

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