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BMC Medical Imaging
Published in: BMC Medical Imaging 1/2016

Open Access 01-12-2016 | Technical advance

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

Authors: Jan L. Bruse, Kristin McLeod, Giovanni Biglino, Hopewell N. Ntsinjana, Claudio Capelli, Tain-Yen Hsia, Maxime Sermesant, Xavier Pennec, Andrew M. Taylor, Silvia Schievano, for the Modeling of Congenital Hearts Alliance (MOCHA) Collaborative Group

Published in: BMC Medical Imaging | Issue 1/2016

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Abstract

Background

Medical 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.

Methods

Steps 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.

Results

The 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.

Conclusions

The 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.
Appendix
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Literature
1.
Lamata P, Casero R, Carapella V, Niederer SA, Bishop MJ, Schneider JE, et al. Images as drivers of progress in cardiac computational modelling. Prog Biophys Mol Biol. 2014;115:198–212.CrossRefPubMedPubMedCentral
2.
Craiem D, Chironi G, Redheuil A, Casciaro M, Mousseaux E, Simon A, et al. Aging Impact on Thoracic Aorta 3D Morphometry in Intermediate-Risk Subjects: Looking Beyond Coronary Arteries with Non-Contrast Cardiac CT. Ann Biomed Eng. 2012;40:1028–38.CrossRefPubMed
3.
Young AA, Frangi AF. Computational cardiac atlases: from patient to population and back. Exp Physiol. 2009;94:578–96.CrossRefPubMed
4.
Lamata P, Lazdam M, Ashcroft A, Lewandowski AJ, Leeson P, Smith N. Computational mesh as a descriptor of left ventricular shape for clinical diagnosis. Computing in Cardiology Conference. 2013;2013:571–4.
5.
Cootes T, Hill A, Taylor C, Haslam J. Use of active shape models for locating structures in medical images. Image Vision Computing. 1994;12:355–65.CrossRef
6.
Remme E, Young AA, Augenstein KF, Cowan B, Hunter PJ. Extraction and quantification of left ventricular deformation modes. IEEE Trans Biomed Eng. 2004;51:1923–31.CrossRefPubMed
7.
Lewandowski AJ, Augustine D, Lamata P, Davis EF, Lazdam M, Francis J, et al. Preterm Heart in Adult Life Cardiovascular Magnetic Resonance Reveals Distinct Differences in Left Ventricular Mass, Geometry, and Function. Circulation. 2013;127:197–206.CrossRefPubMed
8.
9.
10.
Durrleman S, Pennec X, Trouvé A, Ayache N. Measuring brain variability via sulcal lines registration: a diffeomorphic approach. Med Image Comput Comput Assist Interv. 2007;10:675–82.PubMed
11.
Mansi T, Durrleman S, Bernhardt B, Sermesant M, Delingette H, Voigt I, et al. A Statistical Model of Right Ventricle in Tetralogy of Fallot for Prediction of Remodelling and Therapy Planning. In: Yang G-Z, Hawkes D, Rueckert D, Noble A, Taylor C, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. Berlin: Springer; 2009. p. 214–21. Available from: http://​link.​springer.​com/​chapter/​10.​1007/​978-3-642-04268-3_​27.CrossRef
12.
Mansi T, Voigt I, Leonardi B, Pennec X, Durrleman S, Sermesant M, et al. A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot. IEEE Trans Med Imaging. 2011;30:1605–16.CrossRefPubMed
13.
Leonardi B, Taylor AM, Mansi T, Voigt I, Sermesant M, Pennec X, et al. Computational modelling of the right ventricle in repaired tetralogy of Fallot: can it provide insight into patient treatment? Eur Heart J Cardiovasc Imaging. 2013;14:381–6.CrossRefPubMedPubMedCentral
14.
Durrleman S, Pennec X, Trouvé A, Ayache N. Statistical models of sets of curves and surfaces based on currents. Med Image Anal. 2009;13:793–808.CrossRefPubMed
15.
O’Sullivan J. Late Hypertension in Patients with Repaired Aortic Coarctation. Curr Hypertens Rep. 2014;16:1–6.
16.
Vergales JE, Gangemi JJ, Rhueban KS, Lim DS. Coarctation of the Aorta - The Current State of Surgical and Transcatheter Therapies. Curr Cardiol Rev. 2013;9:211–9.CrossRefPubMedPubMedCentral
17.
Ou P, Bonnet D, Auriacombe L, Pedroni E, Balleux F, Sidi D, et al. Late systemic hypertension and aortic arch geometry after successful repair of coarctation of the aorta. Eur Heart J. 2004;25:1853–9.CrossRefPubMed
18.
De Caro E, Trocchio G, Smeraldi A, Calevo MG, Pongiglione G. Aortic Arch Geometry and Exercise-Induced Hypertension in Aortic Coarctation. Am J Cardiol. 2007;99:1284–7.CrossRefPubMed
19.
Lee MGY, Kowalski R, Galati JC, Cheung MMH, Jones B, Koleff J, et al. Twenty-four-hour ambulatory blood pressure monitoring detects a high prevalence of hypertension late after coarctation repair in patients with hypoplastic arches. J Thorac Cardiovasc Surg. 2012;144:1110–8.CrossRefPubMed
20.
21.
22.
23.
Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. Neuroimage. 2014;101:35–49.CrossRefPubMedPubMedCentral
24.
Ntsinjana HN, Biglino G, Capelli C, Tann O, Giardini A, Derrick G, et al. Aortic arch shape is not associated with hypertensive response to exercise in patients with repaired congenital heart diseases. J Cardiovasc Magn Reson. 2013;15:101.CrossRefPubMedPubMedCentral
25.
Schievano S, Migliavacca F, Coats L, Khambadkone S, Carminati M, Wilson N, et al. Percutaneous Pulmonary Valve Implantation Based on Rapid Prototyping of Right Ventricular Outflow Tract and Pulmonary Trunk from MR Data. Radiology. 2007;242:490–7.CrossRefPubMed
26.
Casciaro ME, Craiem D, Chironi G, Graf S, Macron L, Mousseaux E, et al. Identifying the principal modes of variation in human thoracic aorta morphology. J Thorac Imaging. 2014;29:224–32.CrossRefPubMed
27.
Bosmans B, Huysmans T, Wirix-Speetjens R, Verschueren P, Sijbers J, Bosmans J, et al. Statistical Shape Modeling and Population Analysis of the Aortic Root of TAVI Patients. J Med Devices. 2013;7:040925.CrossRef
28.
Zhao F, Zhang H, Wahle A, Thomas MT, Stolpen AH, Scholz TD, et al. Congenital Aortic Disease: 4D Magnetic Resonance Segmentation and Quantitative Analysis. Med Image Anal. 2009;13:483–93.CrossRefPubMedPubMedCentral
29.
Antiga L, Piccinelli M, Botti L, Ene-Iordache B, Remuzzi A, Steinman DA. An image-based modeling framework for patient-specific computational hemodynamics. Med Biol Eng Comput. 2008;46:1097–112.CrossRefPubMed
30.
Piccinelli M, Veneziani A, Steinman DA, Remuzzi A, Antiga L. A framework for geometric analysis of vascular structures: application to cerebral aneurysms. IEEE Trans Med Imaging. 2009;28:1141–55.CrossRefPubMed
31.
Antiga L, Steinman DA. Robust and objective decomposition and mapping of bifurcating vessels. IEEE Trans Med Imaging. 2004;23:704–13.CrossRefPubMed
32.
Besl PJ, McKay ND. A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell. 1992;14:239–56.CrossRef
33.
Heimann T, Meinzer H-P. Statistical shape models for 3D medical image segmentation: A review. Med Image Anal. 2009;13:543–63.CrossRefPubMed
34.
Singh N, Thomas Fletcher P, Samuel Preston J, King RD, Marron JS, Weiner MW, et al. Quantifying anatomical shape variations in neurological disorders. Med Image Anal. 2014;18:616–33.CrossRefPubMed
35.
Mathworks. MATLAB v2014 Documentation - Cook’s Distance. Natick, MA. 2014;
36.
Joliffe IT. Principal Component Analysis. 2nd ed. Inc.: Springer-Verlag New York; 2002.
37.
Canniffe C, Ou P, Walsh K, Bonnet D, Celermajer D. Hypertension after repair of aortic coarctation — A systematic review. Int J Cardiol. 2013;167:2456–61.CrossRefPubMed
38.
Lützen J. De Rham’s Currents. In The Prehistory of the Theory of Distributions. [Studies in the History of Mathematics and Physical Sciences, vol. 7]. Springer New York; 1982:144–7.
39.
Beg MF, Miller MI, Trouvé A, Younes L. Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. Int J Comput Vision. 2005;61:139–57.
40.
Bookstein FL. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989;11:567–85.
Metadata
Title
A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta
Authors
Jan L. Bruse
Kristin McLeod
Giovanni Biglino
Hopewell N. Ntsinjana
Claudio Capelli
Tain-Yen Hsia
Maxime Sermesant
Xavier Pennec
Andrew M. Taylor
Silvia Schievano
for the Modeling of Congenital Hearts Alliance (MOCHA) Collaborative Group
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2016
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-016-0142-z

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