Abstract
Purpose
The coarctation of the aorta (CoA), a local narrowing of the aortic arch, accounts for 7 % of all congenital heart defects. Stenting is a recommended therapy to reduce the pressure gradient. This procedure is associated with complications such as the development of adverse flow conditions. A computer-aided treatment planning based on flow simulations can help to predict possible complications. The virtual stent planning is an important, intermediate step in the treatment planning pipeline. We present a novel approach that automatically suggests a stent setup and provides a set of intuitive parameters that allow for an interactive adaption of the suggested stent placement and induced deformation.
Methods
A high-quality mesh and a centerline are automatically generated. The stent-induced deformation is realized through a deformation of the centerline and a vertex displacement with respect to the deformed centerline and additional stent parameters. The parameterization is automatically derived from the underlying data and can be optionally altered through a condensed set of clinically sound parameters.
Results
The automatic deformation can be generated in about 25 s on a consumer system. The interactive adaption can be performed in real time. Compared with manual expert reconstructions of the stented vessel section, the mean difference of vessel path and diameter is below 1 mm.
Conclusion
Our approach enables a medical user to easily generate a plausibly deformed vessel mesh which is necessary as input for a simulation-based treatment planning of CoA.
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References
Doshi AR, Rao PS (2012) Coarctation of aorta-management options and decision making. Pediatr Therapeut S 5:006. doi:10.4172/2161-0665.S5-006
Forbes TJ, Kobayashi D (2013) Stenting coarctation of the aorta. Card Interv Today Jan/Feb2013:38–44
Feltes TF, Bacha E, Beekman RH (2011) Indications for cardiac catheterization and intervention in pediatric cardiac disease. Circulation 123:2607–2652. doi:10.1161/CIR.0b013e31821b1f10
Waltham M, Agrawal V, Bowman L, Hughes C, White GH (2005) Right arm ischemia following intentional stent-graft coverage of an anomalous right subclavian artery. J Endovasc Ther 12:110–114. doi:10.1583/04-1374R.1
Godart F (2011) Intravascular stenting for the treatment of coarctation of the aorta in adolescent and adult patients. Arch Cardiovasc Dis 104:627–635. doi:10.1016/j.acvd.2011.08.005
Markl M, Harloff A, Bley TA, Zaitsev M, Jung B, Weigang E, Langer M, Hennig J, Frydrychowicz A (2007) Time-resolved 3D MR velocity mapping at 3T: improved navigator-gated assessment of vascular anatomy and blood flow. J Magn Reson Imaging 25:824–831. doi:10.1002/jmri.20871
Hahn HK, Peitgen HO (2003) IWT-interactive watershed transform: a hierarchical method for efficient interactive and automated segmentation of multidimensional gray-scale images. Proc SPIE 5032:642–653. doi:10.1117/12.481097
Meier S, Hennemuth A, Drexl J, Bock J, Jung B, Preusser T (2012) A fast and noise-robust method for computation of intravascular pressure difference maps from 4D PC-MRI Data. Proc STACOM, pp 215–224. doi:10.1007/978-3-642-36961-2_25
Hennemuth A, Friman O, Schumann C (2011) Fast interactive exploration of 4D MRI flow data. Proceedings of SPIE. doi:10.1117/12.878202
Meier S, Hennemuth A, Tchipev N (2011) Towards patient-individual blood flow simulations based on PC-MRI measurements. Lecture notes in informatics 192
Ghriallais RN, Bruzzi M (2014) Self-expanding stent modelling and radial force accuracy. Comput Methods Biomech Biomed Engin 17:318–333. doi:10.1080/10255842.2012.683427
LaDisa JF Jr, Olson LE, Guler I, Hettrick DA, Audi SH, Kersten JR, Warltier DC, Pagel PS (2004) Stent design properties and deployment ratio influence indexes of wall shear stress: a three-dimensional computational fluid dynamics investigation within a normal artery. J Appl Physiol 97:424–430. doi:10.1152/japplphysiol.01329.2003
Kim M, Levy EI, Meng H, Hopkins LN (2007) Quantification of hemodynamic changes induced by virtual placement of multiple stents across a wide-necked basilar trunk aneurysm. Neurosurg 61:1305–1313. doi:10.1227/01.NEU.0000280168.25968.49
Janiga G, Rössl C, Skalej M, Thévenin D (2013) Realistic virtual intracranial stenting and computational fluid dynamics for treatment analysis. J Biomech 46:7–12. doi:10.1016/j.jbiomech.2012.08.047
Larrabide I, Kim M, Augsburger L, Villa-Uriol MC, Rüfenacht D, Frangi AF (2012) Fast virtual deployment of self-expandable stents: method and in vitro evaluation for intracranial aneurysmal stenting. Med Image Anal 16:721–730. doi:10.1016/j.media.2010.04.009
Gundert TJ, Shadden SC, Williams AR, Koo BK, Feinstein JA, Ladisa JF Jr (2011) A rapid and computationally inexpensive method to virtually implant current and next-generation stents into subject-specific computational fluid dynamics models. Ann Biomed Eng 39:1423–1437. doi:10.1007/s10439-010-0238-5
Augsburger L, Reymond P, Rufenacht DA, Stergiopulos N (2011) Intracranial stents being modeled as a porous medium: flow simulation in stented cerebral aneurysms. Ann Biomed Eng 39:850–63. doi:10.1007/s10439-010-0200-6
Demirci S, Lee SL, Radeva P, Unal G (2012) 1st International MICCAI-workshop on computer assisted stenting. Proceedings of MICCAI-STENT’12
Balocco S, Gatta C, Demirci S, Lee SL, Tangen GA (2013) 2nd International MICCAI-workshop on computer assisted stenting. Proceedings of MICCAI-STENT’13
Ford MD, Hoi Y, Piccinelli M, Antiga L, Steinman DA (2009) An objective approach to digital removal of saccular aneurysms: technique and applications. Br J Radiol 82:55–61. doi:10.1259/bjr/67593727
Egger J, Großkopf S, Freisleben B (2009) Virtual stenting for carotid stenosis with elastic artery wall modeling. Proceedings of IFMBE, pp 2499–2502. doi:10.1007/978-3-540-89208-3_599
Xionga G, Choib G, Taylor CA (2012) Virtual interventions for image-based blood flow computation. Comput Aid Des 44:3–14. doi:10.1016/j.cad.2011.01.004
Antiga L, Piccinelli M, Botti L, Ene-Iordache B, Remuzzi A, Steinman DA (2008) An image-based modeling framework for patient-specific computational hemodynamics. Med Biol Eng Comput 46:1097–112. doi:10.1007/s11517-008-0420-1
Pébay P, Baker TJ (2003) Analysis of triangle quality measures. J Math Comp 72:1817–1839. doi:10.1090/S0025-5718-03-01485-6
Schroeder W, Martin K, Lorensen B (1996) The visualization toolkit—an object-oriented approach to 3D graphics. Prentice Hall PTR, Upper Saddle River, N.J
Ritter F, Boskamp T, Homeyer A (2011) Medical image analysis. IEEE Pulse 2:60–70. doi:10.1109/MPUL.2011.942929
Goubergrits L, Riesenkampff E, Yevtushenko P, Schaller J, Kertzscher U, Hennemuth A, Berger F, Schubert S, Kuehne T (2014) MRI-based computational fluid dynamics for diagnosis and treatment prediction: clinical validation study in patients with coarctation of aorta. J Magn Reson Imaging. doi:10.1002/jmri.24639
Zhang Z (1994) Iterative point matching for registration of free-form curves and surfaces. Int J Comput Vis 13:119–152. doi:10.1007/BF01427149
Carrascosa P, Capuñay C, Deviggiano A, Rodríguez-Granillo GA, Sagarduy MI, Cortines P, Carrascosa J, Parodi JC (2013) Thoracic aorta cardiac-cycle related dynamic changes assessed with a 256-slice CT scanner. Cardiovasc Diagn Ther 3:125–128. doi:10.3978/j.issn.2223-3652.2013.07.02
Acknowledgments
This work is part of the EU project CARDIOPROOF (partially funded by the European Commission under ICT-2013.5.2, Grant Agreement: 611232).
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The authors declare that they have no conflict of interest.
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Neugebauer, M., Glöckler, M., Goubergrits, L. et al. Interactive virtual stent planning for the treatment of coarctation of the aorta. Int J CARS 11, 133–144 (2016). https://doi.org/10.1007/s11548-015-1220-3
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DOI: https://doi.org/10.1007/s11548-015-1220-3