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3D printing of cardiac structures from medical images: an overview of methods and interactive tools

  • Francesca Uccheddu
  • Monica CarfagniEmail author
  • Lapo Governi
  • Rocco Furferi
  • Yary Volpe
  • Erica Nocerino
Original Paper

Abstract

The percutaneous interventions in the treatment of structural heart diseases represent nowadays a viable option for patients at high risk for surgery. However, unlike during the traditional open heart surgery, the heart structures to be corrected are not directly visualized by the physician during the interventions. The interpretation of the available medical images is often a demanding task and needs specific skills i.e. clinical experience and complex radiological and echocardiographic analysis. The new trend for cardiovascular diagnosis, surgical planning and intervention is, today, mutually connected with most recent developments in the field of 3D acquisition, interactive modelling and rapid prototyping techniques. This is particularly true when dealing with complex heart diseases since 3D-based techniques can really help in providing an accurate planning of the intervention and to support surgical intervention. To help the research community in confronting with this new trend in medical science, the present work provides an overview on most recent approaches and methodologies for creating physical prototypes of patient-specific cardiac structures, with particular reference to most critical phases such as: 3D image acquisition, interactive image segmentation and restoration, interactive 3D model reconstruction, physical prototyping through additive manufacturing. To this purpose, first, recent techniques for image enhancement to highlight anatomical structures of interest are presented together with the current state of the art of interactive image segmentation. Finally, most suitable techniques for prototyping the retrieved 3D model are investigated so as to derive a number of criteria for manufacturing prototypes useful for planning the medical intervention.

Keywords

Rapid prototyping 3D modelling Medical imagery Heart Cardiovascular diseases Surgical planning 

References

  1. 1.
    Moons, P., Engelfriet, P., Kaemmerer, H., Meijboom, F.J., Oechslin, E., Mulder, B.J.M.: Delivery of care for adult patients with congenital heart disease in Europe: results from the Euro Heart Survey. Eur. Heart J. 27(11), 1324–1330 (2006)CrossRefGoogle Scholar
  2. 2.
    Webb, C.L., Jenkins, K.J., Karpawich, P.P., Bolger, A.F., Donner, R.M., Allen, H.D., Barst, R.J.: Collaborative care for adults with congenital heart disease. Circulation 105(19), 2318–2323 (2002)CrossRefGoogle Scholar
  3. 3.
    Liverani, A., Leali, F., Pellicciari, M.: Real-time 3D features reconstruction through monocular vision. Int. J. Interact. Des. Manuf. 4(2), 103–112 (2010)CrossRefGoogle Scholar
  4. 4.
    Furferi, R., Governi, L.: Machine vision tool for real-time detection of defects on textile raw fabrics. J. Text. Inst. 99(1), 57–66 (2008)CrossRefGoogle Scholar
  5. 5.
    Renzi, C., Leali, F., Cavazzuti, M., Andrisano, A.O.: A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems. Int. J. Adv. Manuf. Technol. 72(1–4), 403–418 (2014)CrossRefGoogle Scholar
  6. 6.
    Ploch, C.C., Mansi, C.S., Jayamohan, J., Kuhl, E.: Using 3D printing to create personalized brain models for neurosurgical training and preoperative planning. World Neurosurg. 90, 668–674 (2016)CrossRefGoogle Scholar
  7. 7.
    Burdall, O.C., Makin, E., Davenport, M., Ade-Ajayi, N.: 3D printing to simulate laparoscopic choledochal surgery. J. Pediatr. Surg. 51(5), 828–831 (2016)CrossRefGoogle Scholar
  8. 8.
    Wang, Y.T., Yang, X.J., Yan, B., Zeng, T.H., Qiu, Y.Y., Chen, S.J.: Clinical application of three-dimensional printing in the personalized treatment of complex spinal disorders. Chin. J. Traumatol. 19(1), 31–34 (2016)CrossRefGoogle Scholar
  9. 9.
    Giannopoulos, A.A., Mitsouras, D., Yoo, S.-J., Liu, P.P., Chatzizisis, Y.S., Rybicki, F.J.: Applications of 3D printing in cardiovascular diseases. Nat. Rev. Cardiol. 13(12), 701–718 (2016)CrossRefGoogle Scholar
  10. 10.
    Nocerino, E., Remondino, F., ccheddu, F., Gallo, M., Gerosa, G.: 3D modelling and rapid prototyping for cardiovascular surgical planning—two case studies. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 41, 887–893 (2016)CrossRefGoogle Scholar
  11. 11.
    Zhang, H., et al.: 4-D cardiac MR image analysis: left and right ventricular morphology and function. IEEE Trans. Med. Imaging 29(2), 350–364 (2010)CrossRefGoogle Scholar
  12. 12.
    Wu, J., Simon, M.A., Brigham, J.C.: A comparative analysis of global shape analysis methods for the assessment of the human right ventricle. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 4(6), 327–343 (2016)Google Scholar
  13. 13.
    Punithakumar, Kumaradevan, et al.: Right ventricular segmentation in cardiac MRI with moving mesh correspondences. Comput. Med. Imaging Graph. 43, 15–25 (2015)CrossRefGoogle Scholar
  14. 14.
    Cappetti, N., Naddeo, A., Naddeo, F., Solitro, G.F.: Finite elements/Taguchi method based procedure for the identification of the geometrical parameters significantly affecting the biomechanical behavior of a lumbar disc. Comput. Methods Biomech. Biomed. Eng. (2015). doi: 10.1080/10255842.2015.1128529 CrossRefGoogle Scholar
  15. 15.
    Weeks, A.R.: Fundamentals of electronic image processing. SPIE Optical Engineering Press, Bellingham (1996)Google Scholar
  16. 16.
    Kahn Jr., C.E., Carrino, J.A., Flynn, M.J., Peck, D.J., Horii, S.C.: DICOM and radiology: past, present, and future. J. Am. Coll. Radiol. 4(9), 652–657 (2007)CrossRefGoogle Scholar
  17. 17.
    Rohrer, M., Bauer, H., Mintorovitch, J., Requardt, M., Weinmann, H.J.: Comparison of magnetic properties of MRI contrast media solutions at different magnetic field strengths. Invest. Radiol. 40(11), 715–724 (2005)CrossRefGoogle Scholar
  18. 18.
    Kuppusamy, P., Zweier, J.L.: A forward-subtraction procedure for removing hyperfine artifacts in electron par-amagnetic resonance imaging. Magn. Reson. Med. 35(3), 316–322 (1996)CrossRefGoogle Scholar
  19. 19.
    Bersvendsen, J., Toews, M., Danudibroto, A., Wells, W.M., Urheim, S., Estépar, R.S.J., Samset, E.: Robust spatio-Temporal registration of 4D cardiac ultrasound sequences. In: Progress in Biomedical Optics and Imaging—Proceedings of SPIE, 9790, art. no. 97900F (2016)Google Scholar
  20. 20.
    Renaud, J.M., Yip, K., Guimond, J., Trottier, M., Pibarot, P., Turcotte, E., Maguire, C., Lalonde, L., Gulenchyn, K., Farncombe, T., Wisenberg, G., Moody, J., Lee, B., Port, S.C., Turkington, T.G., Beanlands, R.S., Kemp, R.A.: Characterization of 3-dimensional PET systems for accurate quantification of myocardial blood flow. J. Nucl. Med. 58(1), 103–109 (2017)CrossRefGoogle Scholar
  21. 21.
    Stradiotti, P., Curti, A., Castellazzi, G., Zerbi, A.: Metal-related artifacts in instrumented spine. Techniques for reducing artifacts in CT and MRI: state of the art. Eur. Spine J. 18(SUPPL. 1), S102–S108 (2009)CrossRefGoogle Scholar
  22. 22.
    Hill, D.L., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Phys. Med. Biol. 46(3), R1 (2001)CrossRefGoogle Scholar
  23. 23.
    Motwani, M.C., Gadiya, M.C., Motwani, R.C., Harris, F.C.: Survey of image denoising techniques. In: Proceedings of GSPX, pp. 27–30 (2004)Google Scholar
  24. 24.
    Draa, A., Benayad, Z., Djenna, F.Z.: An opposition-based firefly algorithm for medical image contrast enhancement. Int. J. Inf. Commun. Technol. 7(4–5), 385–405 (2015)Google Scholar
  25. 25.
    Maini, R., Himanshu, A.: A comprehensive review of image enhancement techniques. (2010). arXiv:1003.4053
  26. 26.
    Belaroussi, B., Milles, J., Carme, S., Zhu, Y.M., Benoit-Cattin, H.: Intensity non-uniformity correction in MRI: existing methods and their validation. Med. Image Anal. 10(2), 234–246 (2006)CrossRefGoogle Scholar
  27. 27.
    Vovk, U., Pernus, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 26(3), 405–421 (2007)CrossRefGoogle Scholar
  28. 28.
    Ashburner, Friston: K.J.: Unified segmentation. NeuroImage 26(3), 839–851 (2005)CrossRefGoogle Scholar
  29. 29.
    Johnston, B., Atkins, M.S., Mackiewich, B., Anderson, M.: Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE Trans. Med. Imaging 15(2), 154–169 (1996)CrossRefGoogle Scholar
  30. 30.
    Axel, L., Costantini, J., Listerud, J.: Intensity correction in surface-coil MR imaging. Am. J. Roentgenol. 148(2), 418–420 (1987)CrossRefGoogle Scholar
  31. 31.
    Hou, Z.: A review on MR image intensity inhomogeneity correction. Int. J. Biomed. Imaging 2006, 49515 (2006). Doi: 10.1155/IJBI/2006/49515
  32. 32.
    Edelstein, W.A., Bottomley, P.A., Pfeifer, L.M.: A signal-to-noise calibration procedure for NMR imaging systems. Med. Phys. 11(2), 180–185 (1984)CrossRefGoogle Scholar
  33. 33.
    Rice, S.O.: Mathematical analysis of random noise. Bell Syst. Tech. J. 23, 282 (1944). (Reprinted by N. Wax, Selected Papers on Noise and Stochastic Process, Dover Publication, 1954, QA273W3)Google Scholar
  34. 34.
    Gudbjartsson, H., Patz, S.: The Rician distribution of noisy MRI data. Magnetic resonance in medicine? Soc. Magn. Reson. Med. 34(6), 910–914 (1995)CrossRefGoogle Scholar
  35. 35.
    Zhang, G., Yan, P., Zhao, H., Zhang, X.A: contrast enhancement algorithm for low-dose CT images based on local histogram equalization. In: 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008, art. no. 4535828, pp. 2462–2465 (2008)Google Scholar
  36. 36.
    Tan, T.L., Sim, K.S., Tso, C.P., Chong, A.K.: Contrast enhancement of computed tomography images by adaptive histogram equalization-application for improved ischemic stroke detection. Int. J. Imaging Syst. Technol. 22(3), 153–160 (2012)CrossRefGoogle Scholar
  37. 37.
    Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43, 1–8 (1997)CrossRefGoogle Scholar
  38. 38.
    Martorelli, M.: A new approach in CT artifact removal: three cases study in maxillofacial surgery. Int. J. Interact. Des. Manuf. 7(2), 115–124 (2013)CrossRefGoogle Scholar
  39. 39.
    Ortiz, S.H., Contreras, T.: Chiu, Fox, M.D.: Ultrasound image enhancement: a review. Biomed. Signal Process. Control 7(5), 419–428 (2012)Google Scholar
  40. 40.
    Chen, Y., Yin, R., Flynn, P., Broschat, S.: Aggressive region growing for speckle reduction in ultra-sound images. Pattern Recognit. Lett. 24(4–5), 677–691 (2003)CrossRefGoogle Scholar
  41. 41.
    Krissian, K., Westin, C.F., Kikinis, R., Vosburgh, K.G.: Oriented speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 16(5), 1412–1424 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  42. 42.
    Yong, Y., Croitoru, M.M., Bidani, A., Zwischenberger, J.B., Clark, J.J.W.: Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images. IEEE Trans. Med. Imaging 25(3), 297–311 (2006)CrossRefGoogle Scholar
  43. 43.
    Finn, S., Glavin, M., Jones, E.: Echocardiographic speckle reduction comparison. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 58(1), 82–101 (2011)CrossRefGoogle Scholar
  44. 44.
    Michailovich, O.V., Adam, D.: A novel approach to the 2-d blind deconvolution problem in medical ultrasound. IEEE Trans. Med. Imaging 24(1), 86–104 (2005)CrossRefGoogle Scholar
  45. 45.
    Goerres, G.W., Hany, T.F., Kamel, E., et al.: Head and neck imaging with PET and PET/CT: artefacts from dental metallic implants. Eur. J. Nucl. Med. Mol. Imaging 29(3), 367–70 (2002)CrossRefGoogle Scholar
  46. 46.
    Lonn, A.H.R.: Evaluation of method to minimize the effect of X-ray contrast in PETCT attenuation correction. In: 2003 IEEE Nuclear Science Symposium, pp. 2220–2221 (2004)Google Scholar
  47. 47.
    Picard, Yani, Thompson, Christopher J.: Motion correction of PET images using multiple acquisition frames. IEEE Trans. Med. Imaging 16(2), 137–144 (1997)CrossRefGoogle Scholar
  48. 48.
    Pal, N.R., Pal, S.H.: A review on image segmentation techniques. Pattern Recognit. 26, 1277–1294 (1993)CrossRefGoogle Scholar
  49. 49.
    Sharma, N., Aggarwal, L.M.: Automated medical image segmentation techniques. J. Med. Phys. 35(1), 3–14 (2010). doi: 10.4103/0971-6203.58777 CrossRefGoogle Scholar
  50. 50.
    McGuinness, K., O’Connor, N.: Toward automated evaluation of interactive segmentation. Comput. Vis. Image Understand. 115(6), 868–884 (2011)CrossRefGoogle Scholar
  51. 51.
    Kalshetti, P., Bundele, M., Rahangdale, P., Jangra, D., Chattopadhyay, C., Harit, G., Elhence, A.: An interactive medical image segmentation framework using iterative refinement. Comput. Biol. Med. 83, 22–33 (2017)CrossRefGoogle Scholar
  52. 52.
    Hassan, K., Dort, J.C., Sutherland, G.R., Chan, S.: Evaluation of software tools for segmentation of temporal bone anatomy. Medicine meets virtual reality 22: NextMed/MMVR22, 220, 130. In: Boykov, Y., Jolly, M.-P. (eds.) Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in n-d Images. ICCV, 2001, vol. 1, pp. 105–112 (2016)Google Scholar
  53. 53.
    Thomas, H.M.T., Devakumar, D., Sasidharan, B., Bowen, S.R., Heck, D.K., Samuel, E.J.J.: Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization. J. Med. Imaging 4(1), 011009–011009 (2017)CrossRefGoogle Scholar
  54. 54.
    Zhao, Y., Zhu, S.C., Luo, S.: Co3 for ultra-fast and accurate interactive segmentation. In: Proceedings of the International Conference on Multimedia, pp. 93–102 (2010)Google Scholar
  55. 55.
    Peng, B., Zhang, L., Zhang, D.: A survey of graph theoretical approaches to image segmentation. Pattern Recognit. 46(3), 1020–1038 (2013)MathSciNetCrossRefGoogle Scholar
  56. 56.
    Ecabert, O., et al.: Segmentation of the heart and great vessels in CT images using a model-based adaptation framework. Med. Image Anal. 15(6), 863–876 (2011)CrossRefGoogle Scholar
  57. 57.
    Schneider, R.J., Perrin, D.P., Vasilyev, N.V., Marx, G.R., Del Nido, P.J., Howe, R.D.: Mitral annulus segmentation from 3D ultrasound using graph cuts. IEEE Trans. Med. Imaging 29(9), 1676–1687 (2010)CrossRefGoogle Scholar
  58. 58.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imaging 27(11), 1668–1681 (2008)CrossRefGoogle Scholar
  59. 59.
    Jolly, M.P.: Automatic segmentation of the left ventricle in cardiac MR and CT images. Int. J. Comput. Vis. 70(2), 151–163 (2006)CrossRefGoogle Scholar
  60. 60.
    Ecabert, O., Peters, J., Weese, J.: Modeling shape variability for full heart segmentation in cardiac computed-tomography images. In: Medical Imaging International Society for Optics and Photonics, pp. 61443R–61443R (2006)Google Scholar
  61. 61.
    Kirişli, H.A., Gupta, V., Kirschbaum, S.W., Rossi, A., Metz, C.T., Schaap, M., van Geuns, R.J., Mollet, N., Lelieveldt, B.P., Reiber, J.H., van Walsum, T.: Comprehensive visualization of multimodal cardiac imaging data for assessment of coronary artery disease: first clinical results of the SMARTVis tool. Int. J. Comput. Assist. Radiol. Surg. 7(4), 557–571 (2012)CrossRefGoogle Scholar
  62. 62.
    Išgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, V.A., Van Ginneken, B.: Multi-atlas-based segmentation with local decision fusion–application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med. Imaging 28(7), 1000–1010 (2009)CrossRefGoogle Scholar
  63. 63.
    Dey, D., Suzuki, Y., Suzuki, S., Ohba, M., Slomka, P.J., Polk, D., Shaw, L.J., Berman, D.S.: Automated quantitation of pericardiac fat from noncontrast CT. Invest. Radiol. 43(2), 145–153 (2008)CrossRefGoogle Scholar
  64. 64.
    Mühlenbruch, G., Das, M., Hohl, C., Wildberger, J.E., Rinck, D., Flohr, T.G., Koos, R., Knackstedt, C., Günther, R.W., Mahnken, A.H.: Global left ventricular function in cardiac CT. Evaluation of an automated 3D region-growing segmentation algorithm. Eur. Radiol. 16(5), 1117–1123 (2006)CrossRefGoogle Scholar
  65. 65.
    Juergens, K.U., Seifarth, H., Range, F., Wienbeck, S., Wenker, M., Heindel, W., Fisch-bach, R.: Automated threshold-based 3D segmentation versus short-axis planimetry for assessment of global left ventricular function with dual-source MDCT. Am. J. Roentgenol. 190(2), 308–314 (2008)CrossRefGoogle Scholar
  66. 66.
    Yalamanchili, R., Dey, D., Kukure, U., Nakazato, R., Berman, D.S., Kakadiaris, I.A.: Knowledge-based quantification of pericardial fat in non-contrast CT data. In: SPIE Medical Imaging International Society for Optics and Photonics, pp. 76231X–76231X (2010)Google Scholar
  67. 67.
    Margeta, J., McLeod, K., Criminisi, A., Ayache, N.: Decision forests for segmentation of the left atrium from 3D MRI. In: International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 49–56 (2013)Google Scholar
  68. 68.
    Lopez-Perez, A., Sebastian, R., Ferrero, J.M.: Three-dimensional cardiac computational modelling: methods, features and applications. BioMed. Eng. Online 14(1), 14–35 (2015)Google Scholar
  69. 69.
    Trunk, P., Mocnik, J., Trobec, R., Gersak, B.: 3D heart model for computer simulations in cardiac surgery. Comput. Biol. Med. 37, 1398–1403 (2007)CrossRefGoogle Scholar
  70. 70.
    Ecabert, O., Peters, J., Schramm, H., Lorenz, C., von Berg, J., Walker, M.J., et al.: Automatic model-based segmentation of the heart in CT images. IEEE Trans. Med. Imaging 27, 1189–1201 (2008)CrossRefGoogle Scholar
  71. 71.
    Ivanc, T., Lorenz, C., von Berg, J., et al.: Segmentation of the heart and great vessels in CT images using a model-based adaptation framework. Med. Image Anal. 15, 863–76 (2011)CrossRefGoogle Scholar
  72. 72.
    Schulte, R.F., Sands, G.B., Sachse, F.B., Dössel, O., Pullan, A.J.: Creation of a human heart model and its customisation using ultrasound images. Biomed. Tech. Eng. 46, 26–28 (2001)CrossRefGoogle Scholar
  73. 73.
    Wenk, J.F., Zhang, Z., Cheng, G., Malhotra, D., Acevedo-Bolton, G., Burger, M., et al.: First finite element model of the left ventricle with mitral valve: insights into ischemic mitral regurgitation. Ann. Thorac. Surg. 89, 1546–53 (2010)CrossRefGoogle Scholar
  74. 74.
    Ruiz-Villa, C.A., Tobón, C., Rodríguez, J.F., Ferrero, J.M., Hornero, F., Saíz, J.: Influence of atrial dilatation in the generation of re-entries caused by ectopic activity in the left atrium. Comput Cardiol. 36, 457–460 (2009)Google Scholar
  75. 75.
    Seemann, G., Höper, C., Sachse, F.B., Dössel, O., Holden, A.V., Zhang, H.: Heterogeneous three-dimensional anatomical and electrophysiological model of human atria. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 364, 1465–1481 (2006)Google Scholar
  76. 76.
    Zhao, J., Butters, T.D., Zhang, H., LeGrice, I.J., Sands, G.B., Smaill, B.H.: Image-based model of atrial anatomy and electrical activation: a computational platform for investigating atrial arrhythmia. IEEE Trans. Med. Imaging 32, 18–27 (2013)CrossRefGoogle Scholar
  77. 77.
    Bishop, M.J., Plank, G., Burton, R.A.B., Schneider, J.E., Gavaghan, D.J., Grau, V., et al.: Development of an anatomically detailed MRI-derived rabbit ventricular model and assessment of its impact on simulations of electrophysiological function. Am. J. Physiol. Heart Circ. Physiol. 298, H699–718 (2010)CrossRefGoogle Scholar
  78. 78.
    Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. In: ACM Siggraph Computer Graphics, vol. 21(4), pp. 163–169. ACM, New York (1987)Google Scholar
  79. 79.
    Han, C.Y., Porembka, D.T., Lin, K.-N.: Method for automatic contour extraction of a cardiac image. U.S. Patent No. 5,457,754. 10 (1995)Google Scholar
  80. 80.
    Di Angelo, L., Di Stefano, P., Giaccari, L.: A new mesh-growing algorithm for fast surface reconstruction. Comput. Aid. Des. 43(6), 639–650 (2011)CrossRefGoogle Scholar
  81. 81.
    Angelo, L.Di, Stefano, P.Di, Giaccari, L.: A fast mesh-growing algorithm for manifold surface reconstruction. Comput. Aid. Des. Appl. 10(2), 197–220 (2013)CrossRefGoogle Scholar
  82. 82.
    Young, P.G., Beresford-West, T.B.H., Coward, S.R.L., Notarberardino, B., Walker, B., Abdul-Aziz, A.: An efficient approach to converting three-dimensional image data into highly accurate computational models. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 366(1878), 3155–3173 (2008)MathSciNetGoogle Scholar
  83. 83.
    Simões, B., Riggio, M., de Amicis, R.: Modeling morphological features of timber from X-ray tomographic images. Int. J. Interact. Des. Manuf. 6(2), 65–73 (2012)CrossRefGoogle Scholar
  84. 84.
    Muscia, R.: Hybrid modelization of intracoronary stents. Int. J. Interact. Des. Manuf. 8(4), 305–315 (2014)CrossRefGoogle Scholar
  85. 85.
    Furferi, R., Governi, L., Palai, M., Volpe, Y.: From unordered point cloud to weighted B-spline—a novel PCA-based method. In: Applications of Mathematics and Computer Engineering—American Conference on Applied Mathematics, AMERICAN-MATH’11, 5th WSEAS International Conference on Computer Engineering and Applications, CEA’11, pp. 146–151 (2011)Google Scholar
  86. 86.
    Governi, L., Furferi, R., Puggelli, L., Volpe, Y.: Improving surface reconstruction in shape from shading using easy-to-set boundary conditions. Int. J. Comput. Vis. Robot. 3(3), 225–247 (2013)CrossRefGoogle Scholar
  87. 87.
    Furferi, R., Governi, L., Palai, M., Volpe, Y.: Multiple incident splines (MISs) algorithm for topological reconstruction of 2D unordered point clouds. Int. J. Math. Comput. Simul. 5(2), 171–179 (2011)zbMATHGoogle Scholar
  88. 88.
    Volpe, Y., Furferi, R., Governi, L., Tennirelli, G.: Computer-based methodologies for semi-automatic 3D model generation from paintings. Int. J. Comput. Aid. Eng. Technol. 6(1), 88–112 (2014)CrossRefGoogle Scholar
  89. 89.
    Colli Franzone, P., Guerri, L., Pennacchio, M., Taccardi, B.: Spread of excitation in 3-D models of the anisotropic cardiac tissue. II. Effects of fiber architecture and ventricular geometry. Math. Biosci. 147, 131–71 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  90. 90.
    Kerckhoffs, R.C.P., Bovendeerd, P.H.M., Kotte, J.C.S., Prinzen, F.W., Smits, K., Arts, T.: Homogeneity of cardiac contraction despite physiological asynchrony of depolarization: a model study. Ann. Biomed. Eng. 31, 536–47 (2003)zbMATHCrossRefGoogle Scholar
  91. 91.
    Romero, D., Sebastian, R., Bijnens, B.H., Zimmerman, V., Boyle, P.M., Vigmond, E.J., et al.: Effects of the purkinje system and cardiac geometry on biventricular pacing: a model study. Ann. Biomed. Eng. 38, 1388–1398 (2010)CrossRefGoogle Scholar
  92. 92.
    Ordas, S., Oubel, E., Sebastian, R., Frangi, A.F.: Computational anatomy atlas of the heart. In: 5th International Symposium on Image Signal Processing and Analysis (ISPA 2007). Istanbul, Turkey. IEEE, pp. 338–342 (2007)Google Scholar
  93. 93.
    Streeter Jr., D.D., Spotnitz, H.M., Patel, D.P., Ross Jr., J., Sonnenblick, E.H.: Fiber orientation in the canine left ventricle during diastole and systole. Circ. Res. 24, 339–347 (1969)CrossRefGoogle Scholar
  94. 94.
    Vandenbroucke, B., Kruth, J.P.: Selective laser melting of biocompatible metals for rapid manufacturing of medical parts. Rapid Prototyp. J. 13(4), 196–203 (2007)CrossRefGoogle Scholar
  95. 95.
    Markwald, R.R.: Organ printing: computer-aided jet-based 3D tissue engineering. Trends Biotechnol. 21(4), 157–161 (2003)CrossRefGoogle Scholar
  96. 96.
    Kruth, J.-P., Leu, M.C., Nakagawa, T.: Progress in additive manufacturing and rapid prototyping. CIRP Ann. Manuf. Technol. 47(2), 525–540 (1998)Google Scholar
  97. 97.
    Utela, B., Storti, D., Anderson, R., Ganter, M.: A review of process development steps for new material systems in three dimensional printing (3DP). J. Manuf. Proc. 10(2), 96–104 (2008)Google Scholar
  98. 98.
    Miner, S., Nield, L.: Left atrial appendage closure guided by personalized 3D-printed cardiac reconstruction. Lett. Editor 8(7), 1004–1006 (2015)Google Scholar
  99. 99.
    Liu, P., Liu, R., Zhang, Y., Liu, Y., Tang, X., Cheng, Y.: The value of 3D printing models of left atrial appendage using real-time 3D transesophageal echocardiographic data in left atrial appendage occlusion: applications toward an era of truly personalized medicine. Cardiology 135(4), 255–261 (2016)CrossRefGoogle Scholar
  100. 100.
    Pepper, J., Petrou, M., Rega, F., Rosendahl, U., Golesworthy, T., Treasure, T.: Implantation of an individually computer-designed and manufactured external support for the Marfan aortic root. In: Multimedia Manual of Cardiothoracic Surgery: MMCTS/European Association for Cardio-Thoracic Surgery, p. mmt004 (2013)Google Scholar
  101. 101.
    Tam, M.D., Latham, T., Brown, J.R.I., Jakeways, M.: Use of a 3D printed hollow aortic model to assist EVAR planning in a case with complex neck anatomy: Potential of 3D printing to improve patient outcome. J. Endovasc. Ther. 21(5), 760–764 (2014)CrossRefGoogle Scholar
  102. 102.
    Chaowu, Y., Hua, L., Xin, S.: Three-dimensional printing as an aid in transcatheter closure of secundum atrial septal defect with rim deficiency: in vitro trial occlusion based on a personalized heart model. Circulation 133(17), e608–e610 (2016)CrossRefGoogle Scholar
  103. 103.
    Yang, D.H., Kang, J.-W., Kim, N., Song, J.-K., Lee, J.-W., Lim, T.-H.: Myocardial 3-dimensional printing for septal myectomy guidance in a patient with obstructive hypertrophic cardiomyopathy. Circulation 132(4), 300–301 (2015)CrossRefGoogle Scholar
  104. 104.
    Giannopoulos, A.A., Steigner, M.L., George, E., Barile, M., Hunsaker, A.R., Rybicki, F.J., Mitsouras, D.: Cardiothoracic applications of 3-dimensional printing. J. Thorac. Imaging 31(5), 253–272 (2016)CrossRefGoogle Scholar
  105. 105.
    Marsden, A.L., Feinstein, J.A.: Computational modeling and engineering in pediatric and congenital heart disease. Curr. Opin. Pediatr. 27(5), 587 (2015)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag France 2017

Authors and Affiliations

  1. 1.Department of Industrial EngineeringUniversità degli Studi di FirenzeFlorenceItaly
  2. 2.Fondazione Bruno KesslerTrentoItaly

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