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Aortic root sizing for transcatheter aortic valve implantation using a shape model parameterisation

  • Bart BosmansEmail author
  • Toon HuysmansEmail author
  • Patricia Lopes
  • Eva Verhoelst
  • Tim Dezutter
  • Peter de Jaegere
  • Jan Sijbers
  • Jos Vander Sloten
  • Johan Bosmans
Original Article
  • 44 Downloads

Abstract

During a transcatheter aortic valve implantation, an axisymmetric implant is placed in an irregularly shaped aortic root. Implanting an incorrect size can cause complications such as leakage of blood alongside or through the implant. The aim of this study was to construct a method that determines the optimal size of the implant based on the three-dimensional shape of the aortic root. Based on the pre-interventional computed tomography scan of 89 patients, a statistical shape model of their aortic root was constructed. The weights associated with the principal components and the volume of calcification in the aortic valve were used as parameters in a classification algorithm. The classification algorithm was trained using the patients with no or mild leakage after their intervention. Subsequently, the algorithms were applied to the patients with moderate to severe leakage. Cross validation showed that a random forest classifier assigned the same size in 65 ± 7% of the training cases, while 57 ± 8% of the patients with moderate to severe leakage were assigned a different size. This initial study showed that this semi-automatic method has the potential to correctly assign an implant size. Further research is required to assess whether the different size implants would improve the outcome of those patients.

Keywords

Statistical shape modelling Aortic root sizing Transcatheter aortic valve implantation 

Notes

Funding information

This work was supported in part by a PhD grant (120198) from the agency for innovation through science and technology (IWT) of the Flemish government.

Compliance with Ethical Standards

Conflict of interest

Prof. Dr. Johan Bosmans and Prof. Dr. Peter de Jaegere are part-time clinical proctor for Medtronic. Prof. Dr. ir. Jos Vander Sloten is a member of the Board of Directors of Materialise N.V. and a shareholder. The remaining authors have no conflicts of interest to declare.

References

  1. 1.
    Alizadeh Sani Z, Shalbaf A, Behnam H, Shalbaf R (2015) Automatic computation of left ventricular volume changes over a cardiac cycle from echocardiography images by nonlinear dimensionality reduction. J Digit Imaging 28(1):91–98CrossRefGoogle Scholar
  2. 2.
    Allen J, Zacur E, Dall’Armellina E, Lamata P, Grau V (2016) Myocardial infarction detection from left ventricular shapes using a random forest. Springer International Publishing, Berlin, pp 180–189Google Scholar
  3. 3.
    Blanke P, Schoepf UJ, Leipsic JA (2013) Ct in transcatheter aortic valve replacement. Radiology 269 (3):650–669CrossRefGoogle Scholar
  4. 4.
    Bose AK, Aitchison JD, Dark JH (2007) Aortic valve replacement in octogenarians. J Cardiothorac Surg 2:33–5CrossRefGoogle Scholar
  5. 5.
    Bosmans B, Collas V, Verhoelst E, Paelinck B, Vander Sloten J, Bosmans J (2016) Morphological characteristics and calcification of the native aortic valve and the relation to significant aortic regurgitation post CoreValve TAVI. Journal of Heart Valve DiseaseGoogle Scholar
  6. 6.
    Bosmans B, Famaey N, Verhoelst E, Bosmans J, Vander Sloten J (2016) A validated methodology for patient specific computational modelling of self-expandable transcatheter aortic valve implantation. J Biomech 49(13):2824–2830CrossRefGoogle Scholar
  7. 7.
    Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–37CrossRefGoogle Scholar
  8. 8.
    den Broeck JV, Vereecke E, Wirix-Speetjens R, Sloten JV (2014) Segmentation accuracy of long bones. Med Eng Phys 36(7):949–953CrossRefGoogle Scholar
  9. 9.
    Bruse JL, Mcleod K, Biglino G, Ntsinjana HN, Capelli C, Hsia TY, Sermesant M, Pennec X, Taylor AM, Schievano S, Hearts C (2016) A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data : assessing arch morphology of repaired coarctation of the aorta. BMC Med Imaging 16(1):40CrossRefGoogle Scholar
  10. 10.
    Buzzatti N, Maisano F, Latib A, Cioni M, Taramasso M, Mussardo M, Colombo A, Alfieri O (2013) Computed tomography-based evaluation of aortic annulus, prosthesis size and impact on early residual aortic regurgitation after transcatheter aortic valve implantation. European Journal of Cardio-thoracic Surgery : Official Journal of the European Association for Cardio-thoracic Surgery 43(1):43–51CrossRefGoogle Scholar
  11. 11.
    Cootes TF, Hill A, Taylor CJ, Hastam J (1994) Use of active shape models for locating structures in medical images. Image Vis Comput 12(6):355–365CrossRefGoogle Scholar
  12. 12.
    Davies RH, Twining CJ, Cootes TF, Waterton JC, Taylor CJ (2002) A minimum description length approach to statistical shape modeling. IEEE Trans Med Imaging 21(5):525–537CrossRefGoogle Scholar
  13. 13.
    Détaint D, Lepage L, Himbert D, Brochet E, Messika-Zeitoun D, Iung B, Vahanian A (2009) Determinants of significant paravalvular regurgitation after transcatheter aortic valve: implantation impact of device and annulus discongruence. JACC. Cardiovascular Interventions 2(9):821–7CrossRefGoogle Scholar
  14. 14.
    Ferrarini L, Palm WM, Olofsen H, Van Der Landen R, Van Buchem MA, Reiber JHC, Admiraal-behloul F (2008) Ventricular shape biomarkers for Alzheimer’s disease in clinical MR images. Magn Reson Med 59(2):260–267CrossRefGoogle Scholar
  15. 15.
    Hayashida K, Bouvier E, Lefévre T, Hovasse T, Morice MC, Chevalier B, Romano M, Garot P, Mylotte D, Farge A, Donzeau-Gouge P, Cormier B (2012) Impact of CT-guided valve sizing on post-procedural aortic regurgitation in transcatheter aortic valve implantation. EuroIntervention : Journal of EuroPCR in Collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology 8 (5):546–55CrossRefGoogle Scholar
  16. 16.
    Holmes DR, Mack MJ, Kaul S, Agnihotri A, Alexander KP, Bailey SR, Calhoon JH, Carabello BA, Desai MY, Edwards FH, Francis GS, Gardner TJ, Kappetein AP, Linderbaum JA, Mukherjee C, Mukherjee D, Otto CM, Ruiz CE, Sacco RL, Smith D, Thomas JD (2012) 2012 ACCF/AATS/SCAI/STS expert consensus document on transcatheter aortic valve replacement. J Am Coll Cardiol 59(13):1200–1254CrossRefGoogle Scholar
  17. 17.
    Hoogendoorn C, Duchateau N, Sanchez-Quintana D, Whitmarsh T, Sukno FM, Craene MD, Lekadir K, Frangi AF (2013) A high-resolution atlas and statistical model of the human heart from multislice ct. IEEE Trans Med Imaging 32(1):28–44CrossRefGoogle Scholar
  18. 18.
    Huysmans T, Sijbers J, Verdonk B (2010) Automatic construction of correspondences for tubular surfaces. IEEE Trans Pattern Anal Mach Intell 32(4):636–51CrossRefGoogle Scholar
  19. 19.
    Jilaihawi H, Kashif M, Fontana G, Furugen A, Shiota T, Friede G, Makhija R, Doctor N, Leon MB, Makkar RR (2012) Cross-sectional computed tomographic assessment improves accuracy of aortic annular sizing for transcatheter aortic valve replacement and reduces the incidence of paravalvular aortic regurgitation. J Am Coll Cardiol 59(14):1275–1286CrossRefGoogle Scholar
  20. 20.
    Kodali SK, Williams MR, Smith CR, Svensson LG, Webb JG, Makkar RR, Fontana GP, Dewey TM, Thourani VH, Pichard AD, Fischbein M, Szeto WY, Lim S, Greason KL, Teirstein PS, Malaisrie SC, Douglas PS, Hahn RT, Whisenant B, Zajarias A, Wang D, Akin JJ, Anderson WN, Leon MB (2012) Two-year outcomes after transcatheter or surgical aortic-valve replacement. N Engl J Med 366 (18):1686–95CrossRefGoogle Scholar
  21. 21.
    Lacko D, Huysmans T, Parizel PM, De Bruyne G, Verwulgen S, Van Hulle MM, Sijbers J (2015) Evaluation of an anthropometric shape model of the human scalp. Appl Ergon 48:70–85CrossRefGoogle Scholar
  22. 22.
    Lekadir K, Albà X, Pereañez M, Frangi AF (2016) Statistical shape modeling using partial least squares: application to the assessment of myocardial infarction. Springer International Publishing, Berlin, pp 130–139Google Scholar
  23. 23.
    Lorenz C, von Berg J (2006) A comprehensive shape model of the heart. Med Image Anal 10(4):657–70CrossRefGoogle Scholar
  24. 24.
    Ltjnen J, Kivist S, Koikkalainen J, Smutek D, Lauerma K (2004) Statistical shape model of atria, ventricles and epicardium from short- and long-axis {MR} images. Med Image Anal 8(3):371–386. Medical Image Computing and Computer-Assisted Intervention - {MICCAI} 2003CrossRefGoogle Scholar
  25. 25.
    Magnenat-thalmann N, Seo H, Cordier F (2004) Automatic modeling of virtual humans and body clothing. J Comput Sci Technol 19(5):575–584CrossRefGoogle Scholar
  26. 26.
    Paulsen RR, Larsen R, Nielsen C, Laugesen S, Ersbøll B (2002) Building and testing a statistical shape model of the human ear canal. Medical Image Computing and Computer-Assisted Intervention 2489:373–380Google Scholar
  27. 27.
    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
  28. 28.
    Pinto C, Çimen S, Gooya A, Lekadir K, Frangi AF (2016) Joint clustering and component analysis of spatio-temporal shape patterns in myocardial infarction. Springer International Publishing, Berlin, pp 171–179Google Scholar
  29. 29.
    Ren Y, Wang L, Gao Y, Tang Z, Chen KC, Li J, Shen SGF, Yan J, Lee PKM, Chow B, Xia JJ, Shen D (2014) Estimating anatomically-correct reference model for craniomaxillofacial deformity via sparse representation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8674 LNCS(PART 2):73–80Google Scholar
  30. 30.
    Schultz CJ, Moelker A, Piazza N, Tzikas A, Otten A, Nuis RJ, Neefjes LA, Van Geuns RJ, De Feyter P, Krestin G, Serruys PW, De Jaegere PPT (2010) Three dimensional evaluation of the aortic annulus using multislice computer tomography: are manufacturer’s guidelines for sizing for percutaneous aortic valve replacement helpful?. Eur Heart J 31(7):849–856CrossRefGoogle Scholar
  31. 31.
    Sellers R, Levy M, Amplatz K, Lillehei CW (1964) Left retrograde cardioangiography in acquired cardiac disease: technique, indications and interpretations in 700 Cases. Am J Cardiol 14(October):437–447CrossRefGoogle Scholar
  32. 32.
    Styner MA, Rajamani KT, Nolte LP, Zsemlye G, Székely G, Taylor CJ, Davies RH (2003) Evaluation of 3D correspondence methods for model building. Proc Information Processing in Medical Imaging 18:63–75CrossRefGoogle Scholar
  33. 33.
    Thorstensen N, tyngier P, Sgonne F, Keriven R (2011) Diffusion maps as a framework for shape modeling. Comput Vis Image Underst 115(4):520–530CrossRefGoogle Scholar
  34. 34.
    Thourani VH, Ailawadi G, Szeto WY, Dewey TM, Guyton RA, Mack MJ, Kron IL, Kilgo P, Bavaria JE (2011) Outcomes of surgical aortic valve replacement in high-risk patients: a multiinstitutional study. Ann Thorac Surg 91(1):49–56CrossRefGoogle Scholar
  35. 35.
    Wang Y, Yuan L, Shi J, Greve A, Ye J, Toga AW, Reiss AL, Thompson PM (2013) Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis. NeuroImage 74:209–230CrossRefGoogle Scholar
  36. 36.
    Young AA, Frangi AF (2009) Computational cardiac atlases : from patient to population and back. Exp Physiol 94(5):578–596CrossRefGoogle Scholar
  37. 37.
    Zachow S, Lamecker H, Elsholtz B, Stiller M (2005) Reconstruction of mandibular dysplasia using a statistical 3D shape model. Int Congr Ser 1281:1238–1243CrossRefGoogle Scholar
  38. 38.
    Zhao F, Zhang H, Wahle A, Thomas MT, Stolpen AH, Scholz TD, Sonka M (2009) Congenital aortic disease : 4D magnetic resonance segmentation and quantitative analysis. Med Image Anal 13(3):483–493CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  • Bart Bosmans
    • 1
    • 2
    • 3
    Email author
  • Toon Huysmans
    • 4
    • 5
    Email author
  • Patricia Lopes
    • 1
    • 2
    • 4
  • Eva Verhoelst
    • 2
  • Tim Dezutter
    • 6
    • 7
  • Peter de Jaegere
    • 8
  • Jan Sijbers
    • 4
  • Jos Vander Sloten
    • 1
  • Johan Bosmans
    • 3
  1. 1.KULeuven, Faculty of Engineering ScienceDepartement of Mechanical Engineering, Biomechanics SectionLeuvenBelgium
  2. 2.Materialise N.V.LeuvenBelgium
  3. 3.Faculty of Medicine and Health Sciences, Department of Translational Pathophysiological Research, Cardiovascular diseasesUniversity of AntwerpAntwerpBelgium
  4. 4.iMinds-Vision LabUniversity of AntwerpAntwerpBelgium
  5. 5.Applied Ergonomics and DesignDepartment of Industrial Design, TU DelftDelftThe Netherlands
  6. 6.UGent, IBiTech-bioMMeda, iMinds medical ITGhent UniversityGhentBelgium
  7. 7.FEops N.V.GhentBelgium
  8. 8.Erasmus Medical Center, Thoraxcenter, Departement of CardiologyRotterdamThe Netherlands

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