Computational technology for nasal cartilage-related clinical research and application

Bing Shi , Hanyao Huang

International Journal of Oral Science ›› 2020, Vol. 12 ›› Issue (1) : 21

PDF
International Journal of Oral Science ›› 2020, Vol. 12 ›› Issue (1) : 21 DOI: 10.1038/s41368-020-00089-y
Review Article

Computational technology for nasal cartilage-related clinical research and application

Author information +
History +
PDF

Abstract

Surgeons need to understand the effects of the nasal cartilage on facial morphology, the function of both soft tissues and hard tissues and nasal function when performing nasal surgery. In nasal cartilage-related surgery, the main goals for clinical research should include clarification of surgical goals, rationalization of surgical methods, precision and personalization of surgical design and preparation and improved convenience of doctor–patient communication. Computational technology has become an effective way to achieve these goals. Advances in three-dimensional (3D) imaging technology will promote nasal cartilage-related applications, including research on computational modelling technology, computational simulation technology, virtual surgery planning and 3D printing technology. These technologies are destined to revolutionize nasal surgery further. In this review, we summarize the advantages, latest findings and application progress of various computational technologies used in clinical nasal cartilage-related work and research. The application prospects of each technique are also discussed.

Cite this article

Download citation ▾
Bing Shi, Hanyao Huang. Computational technology for nasal cartilage-related clinical research and application. International Journal of Oral Science, 2020, 12(1): 21 DOI:10.1038/s41368-020-00089-y

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Lavernia L, Brown WE, Wong BJF, Hu JC, Athanasiou KA. Toward tissue-engineering of nasal cartilages. Acta Biomater., 2019, 88: 42-56.

[2]

Pelttari K, Mumme M, Barbero A, Martin I. Nasal chondrocytes as a neural crest-derived cell source for regenerative medicine. Curr. Opin. Biotechnol., 2017, 47: 1-6.

[3]

Bhumiratana S, . Tissue-engineered autologous grafts for facial bone reconstruction. Sci. Transl. Med., 2016, 8: 343ra383.

[4]

Huang H, . Recapitulation of unilateral cleft lip nasal deformity on normal nasal structure: a finite element model analysis. J. Craniofac. Surg., 2018, 29: 2220-2225.

[5]

Huang H, Luo X, Cheng X, Shi B, Li J. Biomechanical simulation of correcting primary unilateral cleft lip nasal deformity. PLoS ONE, 2018, 13

[6]

Huang H, . Mechanical analyses of critical surgical maneuvers in the correction of cleft lip nasal deformity. PLoS ONE, 2018, 13

[7]

Huang H, Cheng X, Luo X, Shi B, Li J. Biomechanical analyses of common suspension sutures in primary cleft lip rhinoplasty. Head Face Med., 2019, 15: 20.

[8]

Marcus JR, . Multimodal characterization of the mature septal deformity and airspace associated with unilateral cleft lip and palate. Plast. Reconstr. Surg., 2019, 143: 865-873.

[9]

Frank-Ito DO, . Computational analysis of the mature unilateral cleft lip nasal deformity on nasal patency. Plast. Reconstr. Surg. Glob. Open, 2019, 7

[10]

Tracy LF, . Impact of endoscopic craniofacial resection on simulated nasal airflow and heat transport. Int. Forum Allergy Rhinol., 2019, 9: 900-909.

[11]

Lefevre N, . A current review of the meniscus imaging: proposition of a useful tool for its radiologic analysis. Radiol. Res. Pract., 2016, 2016: 8329296.

[12]

Sharp HR, Rowe-Jones JM. Assessing outcome in aesthetic rhinoplasty. Clin. Otolaryngol. Allied Sci., 2003, 28: 430-435.

[13]

Pawar SS, Garcia GJM, Kimbell JS, Rhee JS. Objective measures in aesthetic and functional nasal surgery: perspectives on nasal form and function. Facial Plast. Surg., 2010, 26: 320-327.

[14]

Nelson BB, . Recent advances in articular cartilage evaluation using computed tomography and magnetic resonance imaging. Equine Vet. J., 2018, 50: 564-579.

[15]

Joseph JS, Paul AA, Wellington KH. Use of computed tomography for assessing bone mineral density. Neurosurg. Focus, 2014, 37: E4.

[16]

Nelson BB, Goodrich LR, Barrett MF, Grinstaff MW, Kawcak CE. Use of contrast media in computed tomography and magnetic resonance imaging in horses: techniques, adverse events and opportunities. Equine Vet. J., 2017, 49: 410-424.

[17]

Siebelt M, . Clinically applied CT arthrography to measure the sulphated glycosaminoglycan content of cartilage. Osteoarthr. Cartil., 2011, 19: 1183-1189.

[18]

Venäläinen MS, . Quantitative evaluation of the mechanical risks caused by focal cartilage defects in the knee. Sci. Rep., 2016, 6

[19]

Graviero G, Guastini L, Mora R, Salzano G, Salzano FA. The role of three-dimensional CT in the evaluation of nasal structures and anomalies. Eur. Arch. Otorhinolaryngol., 2011, 268: 1163-1167.

[20]

Visscher DO, . MRI and additive manufacturing of nasal alar constructs for patient-specific reconstruction. Sci. Rep., 2017, 7

[21]

Wu J, Yin N. Detailed anatomy of the nasolabial muscle in human fetuses as determined by micro-CT combined with iodine staining. Ann. Plast. Surg., 2016, 76: 111-116.

[22]

Saxena RC, . Comparison of micro-computed tomography and clinical computed tomography protocols for visualization of nasal cartilage before surgical planning for rhinoplasty. JAMA Facial Plast. Surg., 2019, 21: 237-243.

[23]

Lansdown, D. A. & Ma, C. B. Clinical utility of advanced imaging of the knee. J. Orthop. Res. https://doi.org/10.1002/jor.24462.

[24]

Bekkers JE, . Delayed gadolinium enhanced MRI of cartilage (dGEMRIC) can be effectively applied for longitudinal cohort evaluation of articular cartilage regeneration. Osteoarthr. Cartil., 2013, 21: 943-949.

[25]

van Tiel J, . Reproducibility of 3D delayed gadolinium enhanced MRI of cartilage (dGEMRIC) of the knee at 3.0 T in patients with early stage osteoarthritis. Eur. Radiol., 2013, 23: 496-504.

[26]

Wang YX, . T1rho magnetic resonance: basic physics principles and applications in knee and intervertebral disc imaging. Quant. Imaging Med. Surg., 2015, 5: 858-885.

[27]

Mars M, Chelli M, Tbini Z, Ladeb F, Gharbi S. MRI T2 mapping of knee articular cartilage using different acquisition sequences and calculation methods at 1.5 Tesla. Med Princ. Pract., 2018, 27: 443-450.

[28]

Chu CR, . Quantitative magnetic resonance imaging UTE-T2* mapping of cartilage and meniscus healing after anatomic anterior cruciate ligament reconstruction. Am. J. Sports Med., 2014, 42: 1847-1856.

[29]

Madelin G, Lee J-S, Regatte RR, Jerschow A. Sodium MRI: methods and applications. Prog. Nucl. Magn. Reson. Spectrosc., 2014, 79: 14-47.

[30]

Baliyan V, Das CJ, Sharma R, Gupta AK. Diffusion weighted imaging: technique and applications. World J. Radiol., 2016, 8: 785-798.

[31]

Raya JG, . Diffusion-tensor imaging of human articular cartilage specimens with early signs of cartilage damage. Radiology, 2013, 266: 831-841.

[32]

Krishnamoorthy G, Nanga RPR, Bagga P, Hariharan H, Reddy R. High quality three-dimensional gagCEST imaging of in vivo human knee cartilage at 7 Tesla. Magn. Reson. Med., 2017, 77: 1866-1873.

[33]

Wang T, . Nasal chondromesenchymal hamartoma in young children: CT and MRI findings and review of the literature. World J. Surg. Oncol., 2014, 12: 257.

[34]

Tan, H. B. & Rimmer, J. Nasal chondrosarcoma of the lower lateral cartilage. Medicina (Kaunas) 55, https://doi.org/10.3390/medicina55050128 (2019).

[35]

Hoshi K, . Three-dimensional changes of noses after transplantation of implant-type tissue-engineered cartilage for secondary correction of cleft lip-nose patients. Regen. Ther., 2017, 7: 72-79.

[36]

Kleinheinz J, Joos U. Imaging of cartilage and mimic muscles with MRI: anatomic study in healthy volunteers and patients with unilateral cleft lip and palate. Cleft Palate Craniofac. J., 2001, 38: 291-298.

[37]

Mazza E, Barbarino GG. 3D mechanical modeling of facial soft tissue for surgery simulation. Facial Plast. Surg. Clin. N. Am., 2011, 19: 623-637. viii

[38]

Stenner M, Koopmann M, Rudack C. Measuring the nose in septorhinoplasty patients: ultrasonographic standard values and clinical correlations. Eur. Arch. Otorhinolaryngol., 2017, 274: 855-860.

[39]

Gossner J. Sonography of the nasal cartilage: technique and normal anatomy. J. Ultrasound, 2014, 17: 317-319.

[40]

Kerr W, Rowe P, Pierce SG. Accurate 3D reconstruction of bony surfaces using ultrasonic synthetic aperture techniques for robotic knee arthroplasty. Comput. Med. Imaging Graph., 2017, 58: 23-32.

[41]

Gandy JR, Manuel CT, Leary RP, Wong BJ. Quantifying optimal columellar strut dimensions for nasal tip stabilization after rhinoplasty via finite element analysis. JAMA Facial Plast. Surg., 2016, 18: 194-200.

[42]

Manuel CT, Leary R, Protsenko DE, Wong BJ. Nasal tip support: a finite element analysis of the role of the caudal septum during tip depression. Laryngoscope, 2014, 124: 649-654.

[43]

Lee J-S, Lee DC, Ha D-H, Kim SW, Cho D-W. Redefining the septal L-strut to prevent collapse. PLoS ONE, 2016, 11: e0153056-e0153056.

[44]

Byrne N, Velasco Forte M, Tandon A, Valverde I, Hussain T. A systematic review of image segmentation methodology, used in the additive manufacture of patient-specific 3D printed models of the cardiovascular system. JRSM Cardiovasc. Dis., 2016, 5: 2048004016645467-2048004016645467.

[45]

Chang, B., Reighard, C., Flanagan, C., Hollister, S. & Zopf, D. Evaluation of human nasal cartilage nonlinear and rate dependent mechanical properties. J. Biomech. 109549 (2019).

[46]

Lin L-L, Lu Y-J, Fang M-L. Computational modeling of the fluid mechanical environment of regular and irregular scaffolds. Int. J. Autom. Comput., 2015, 12: 529-539.

[47]

Meloni GR, Fisher MB, Stoeckl BD, Dodge GR, Mauck RL. Biphasic finite element modeling reconciles mechanical properties of tissue-engineered cartilage constructs across testing platforms. Tissue Eng. Part A, 2017, 23: 663-674.

[48]

Completo A, Bandeiras C, Fonseca F. Comparative assessment of intrinsic mechanical stimuli on knee cartilage and compressed agarose constructs. Med. Eng. Phys., 2017, 44: 87-93.

[49]

Manuel CT, Harb R, Badran A, Ho D, Wong BJF. Finite element model and validation of nasal tip deformation. Ann. Biomed. Eng., 2017, 45: 829-838.

[50]

Liong K, Lee SJ, Lee HP. Preliminary deformational studies on a finite element model of the nasal septum reveals key areas for septal realignment and reconstruction. J. Med. Eng., 2013, 2013: 250274-250274.

[51]

Lee SJ, Liong K, Lee HP. Deformation of nasal septum during nasal trauma. Laryngoscope, 2010, 120: 1931-1939.

[52]

Chae, Y., Diaz-Valdes, S. H., Lavernia, E. J. & Wong, B. J. in Laser-Tissue. Interact. XII: Photochemical, Photothermal, Photomechanical, Vol. 4257 (eds Duncan, D. D., Johnson, P. C. & Jacques, S. L.) 255–268 (Society of Photo Optical, 2001).

[53]

Manuel CT, Foulad A, Protsenko DE, Sepehr A, Wong BJF. Needle electrode-based electromechanical reshaping of cartilage. Ann. Biomed. Eng., 2010, 38: 3389-3397.

[54]

Oliaei S, . Mechanical analysis of the effects of cephalic trim on lower lateral cartilage stability. Arch. Facial Plast. Surg., 2012, 14: 27-30.

[55]

Leary RP, Manuel CT, Shamouelian D, Protsenko DE, Wong BJF. Finite element model analysis of cephalic trim on nasal tip stability. JAMA Facial Plast. Surg., 2015, 17: 413-420.

[56]

Griffin MF, Premakumar Y, Seifalian AM, Szarko M, Butler PEM. Biomechanical characterisation of the human nasal cartilages; implications for tissue engineering. J. Mater. Sci. Mater. Med., 2016, 27: 11-11.

[57]

Wittek A, Grosland NM, Joldes GR, Magnotta V, Miller K. From finite element meshes to clouds of points: a review of methods for generation of computational biomechanics models for patient-specific applications. Ann. Biomed. Eng., 2016, 44: 3-15.

[58]

Du Q, Wang D. Tetrahedral mesh generation and optimization based on centroidal Voronoi tessellations. Int. J. Numer. Methods Eng., 2003, 56: 1355-1373.

[59]

Yerry MA, Shephard MS. Automatic three-dimensional mesh generation by the modified-octree technique. Int. J. Numer. Methods Eng., 1984, 20: 1965-1990.

[60]

Lee CK, Hobbs RE. Automatic adaptive finite element mesh generation over arbitrary two-dimensional domain using advancing front technique. Comput. Struct., 1999, 71: 9-34.

[61]

Bornemann F, Erdmann B, Kornhuber R. Adaptive multivlevel methods in three space dimensions. Int. J. Numer. Methods Eng., 1993, 36: 3187-3203.

[62]

Zheng, Y. Automatic mesh generation: application to finite element methods, by P. L. George, Wiley, New York, 1991. no. of pages: X + 333. ISBN 0-471-93097-0. Int. J. Numer. Methods Eng. 38, 2483–2484 (1995).

[63]

Hughes TJR, Cottrell JA, Bazilevs Y. Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement. Comput. Methods Appl. Mech. Eng., 2005, 194: 4135-4195.

[64]

Doblaré M, . On the employ of meshless methods in biomechanics. Comput. Methods Appl. Mech. Eng., 2005, 194: 801-821.

[65]

Glass GE, Staruch RMT, Ruston J, East CA, Tan PJ. Beyond the L-strut: redefining the biomechanics of rhinoplasty using topographic optimization modeling. Aesthetic Surg. J., 2019, 39: 1309-1318.

[66]

Protsenko DE, Wong BJ. Laser-assisted straightening of deformed cartilage: numerical model. Lasers Surg. Med., 2007, 39: 245-255.

[67]

Protsenko, D. E. & Wong, B. J. Engineering of a straighter septum: numerical model of mechanical stress relaxation in laser-heated septal cartilage. IEEE. Annual Conference 5399–5402 (2007).

[68]

Mau T, Mau ST, Kim DW. Cadaveric and engineering analysis of the septal L-strut. Laryngoscope, 2007, 117: 1902-1906.

[69]

Lee SJ, Liong K, Tse KM, Lee HP. Biomechanics of the deformity of septal L-struts. Laryngoscope, 2010, 120: 1508-1515.

[70]

Lee JS, Lee DC, Ha DH, Kim SW, Cho DW. Redefining the septal L-strut in septal surgery. PLoS ONE, 2015, 10

[71]

Gizzi A, . Computational modeling and stress analysis of columellar biomechanics. J. Mech. Behav. Biomed. Mater., 2012, 15: 46-58.

[72]

Shamouelian D, . Rethinking nasal tip support: a finite element analysis. Laryngoscope, 2015, 125: 326-330.

[73]

Tjoa T, . A finite element model to simulate formation of the inverted-V deformity. JAMA Facial Plast. Surg., 2016, 18: 136-143.

[74]

Hassan CR, Qin Y-X, Komatsu DE, Uddin SMZ. Utilization of finite element analysis for articular cartilage tissue engineering. Materials (Basel), 2019, 12: 3331.

[75]

Mohammadi H, Mequanint K, Herzog W. Computational aspects in mechanical modeling of the articular cartilage tissue. Proc. Inst. Mech. Eng. Part H, 2013, 227: 402-420.

[76]

Mohan S, Fuller JC, Ford SF, Lindsay RW. Diagnostic and therapeutic management of nasal airway obstruction: advances in diagnosis and treatment. JAMA Facial Plast. Surg., 2018, 20: 409-418.

[77]

Ottaviano G, Fokkens WJ. Measurements of nasal airflow and patency: a critical review with emphasis on the use of peak nasal inspiratory flow in daily practice. Allergy, 2016, 71: 162-174.

[78]

Nathan RA, Eccles R, Howarth PH, Steinsvag SK, Togias A. Objective monitoring of nasal patency and nasal physiology in rhinitis. J. Allergy Clin. Immunol., 2005, 115: S442-S459.

[79]

Spataro E, Most SP. Measuring nasal obstruction outcomes. Otolaryngol. Clin. N. Am., 2018, 51: 883-895.

[80]

Haavisto LE, Sipila JI. Acoustic rhinometry, rhinomanometry and visual analogue scale before and after septal surgery: a prospective 10-year follow-up. Clin. Otolaryngol., 2013, 38: 23-29.

[81]

Moore M, Eccles R. Objective evidence for the efficacy of surgical management of the deviated septum as a treatment for chronic nasal obstruction: a systematic review. Clin. Otolaryngol., 2011, 36: 106-113.

[82]

Andre RF, Vuyk HD, Ahmed A, Graamans K, Nolst Trenite GJ. Correlation between subjective and objective evaluation of the nasal airway. A systematic review of the highest level of evidence. Clin. Otolaryngol., 2009, 34: 518-525.

[83]

Singh A, Patel N, Kenyon G, Donaldson G. Is there objective evidence that septal surgery improves nasal airflow?. J. Laryngol. Otol., 2006, 120: 916-920.

[84]

Leite SHP, Jain R, Douglas RG. The clinical implications of computerised fluid dynamic modelling in rhinology. Rhinology, 2019, 57: 2-9.

[85]

Kumar H, Jain R, Douglas RG, Tawhai MH. Airflow in the human nasal passage and sinuses of chronic rhinosinusitis subjects. PLoS ONE, 2016, 11

[86]

Quadrio M, . Review of computational fluid dynamics in the assessment of nasal air flow and analysis of its limitations. Eur. Arch. Otorhinolaryngol., 2014, 271: 2349-2354.

[87]

Barber DC, Oubel E, Frangi AF, Hose DR. Efficient computational fluid dynamics mesh generation by image registration. Med. Image Anal., 2007, 11: 648-662.

[88]

Leong SC, Chen XB, Lee HP, Wang DY. A review of the implications of computational fluid dynamic studies on nasal airflow and physiology. Rhinology, 2010, 48: 139-145.

[89]

Huang, H. et al. Analysis of velopharyngeal functions using computational fluid dynamics simulations. Ann. Otol. Rhinol. Laryngol. https://doi.org/10.1177/0003489419842217 (2019).

[90]

Huang, H. et al. Computational fluid dynamic analysis of different velopharyngeal closure patterns. Ann. Otol. Rhinol. Laryngol. https://doi.org/10.1177/0003489419879176 (2019).

[91]

Frank-Ito DO, Sajisevi M, Solares CA, Jang DW. Modeling alterations in sinonasal physiology after skull base surgery. Am. J. Rhinol. Allergy, 2015, 29: 145-150.

[92]

Pawar SS, Garcia GJ, Rhee JS. Advances in technology for functional rhinoplasty: the next. Front. Facial Plast. Surg. Clin. N. Am., 2017, 25: 263-270.

[93]

Garcia GJM, Bailie N, Martins DA, Kimbell JS. Atrophic rhinitis: a CFD study of air conditioning in the nasal cavity. J. Appl. Physiol., 2007, 103: 1082-1092.

[94]

Keyhani K, Scherer PW, Mozell MM. Numerical simulation of airflow in the human nasal cavity. J. Biomech. Eng., 1995, 117: 429-441.

[95]

Chen XB, Lee HP, Chong VF, Wang de Y. Numerical simulation of the effects of inferior turbinate surgery on nasal airway heating capacity. Am. J. Rhinol. Allergy, 2010, 24: e118-e122.

[96]

Lindemann J, . Numerical simulation of humidification and heating during inspiration in nose models with three different located septal perforations. Eur. Arch. Otorhinolaryngol., 2016, 273: 1795-1800.

[97]

Yu S, Sun XZ, Liu YX. Numerical analysis of the relationship between nasal structure and its function. Sci. World J., 2014, 2014: 581975.

[98]

Keeler JA, Patki A, Woodard CR, Frank-Ito DO. A computational study of nasal spray deposition pattern in four ethnic groups. J. Aerosol Med. Pulm. Drug Deliv., 2016, 29: 153-166.

[99]

Chen XB, Lee HP, Chong VF, Wang DY. Drug delivery in the nasal cavity after functional endoscopic sinus surgery: a computational fluid dynamics study. J. Laryngol. Otol., 2012, 126: 487-494.

[100]

Zhu JH, . Inspirational airflow patterns in deviated noses: a numerical study. Comput. Methods Biomech. Biomed. Eng., 2013, 16: 1298-1306.

[101]

Sanmiguel-Rojas E, Burgos MA, Esteban-Ortega F. Nasal surgery handled by CFD tools. Int. J. Numer. Methods Biomed. Eng., 2018, 34

[102]

Lee TS, Goyal P, Li C, Zhao K. Computational fluid dynamics to evaluate the effectiveness of inferior turbinate reduction techniques to improve nasal airflow. JAMA Facial Plast. Surg., 2018, 20: 263-270.

[103]

Tan J, . Numerical simulation of normal nasal cavity airflow in Chinese adult: a computational flow dynamics model. Eur. Arch. Otorhinolaryngol., 2012, 269: 881-889.

[104]

de Gabory L, Reville N, Baux Y, Boisson N, Bordenave L. Numerical simulation of two consecutive nasal respiratory cycles: toward a better understanding of nasal physiology. Int. Forum Allergy Rhinol., 2018, 8: 676-685.

[105]

Zhao K, Jiang J. What is normal nasal airflow? A computational study of 22 healthy adults. Int. Forum Allergy Rhinol., 2014, 4: 435-446.

[106]

Schalek P, Hahn A. Anterior septal deviation and contralateral alar collapse. B-ENT, 2011, 7: 185-188.

[107]

Fallahi, H. R., Keyhan, S. O., Fattahi, T. & Zandian, D. Transcutaneous alar rim graft: an effective technique to manage alar deformity. J. Oral Maxillofac. Surg. https://doi.org/10.1016/j.joms.2019.12.002 (2019).

[108]

Khosh MM, Jen A, Honrado C, Pearlman SJ. Nasal valve reconstruction: experience in 53 consecutive patients. Arch. Facial Plast. Surg., 2004, 6: 167-171.

[109]

Bloching, M. B. Disorders of the nasal valve area. GMS Curr. Top. Otorhinolaryngol. Head Neck Surg 6, Doc07 (2007).

[110]

Schroeter JD, Kimbell JS, Asgharian B. Analysis of particle deposition in the turbinate and olfactory regions using a human nasal computational fluid dynamics model. J. Aerosol Med., 2006, 19: 301-313.

[111]

Shadfar S, . Characterization of postoperative changes in nasal airflow using a cadaveric computational fluid dynamics model: supporting the internal nasal valve. JAMA Facial Plast. Surg., 2014, 16: 319-327.

[112]

Brandon BM, . Comparison of airflow between spreader grafts and butterfly grafts using computational flow dynamics in a cadaveric model. JAMA Facial Plast. Surg., 2018, 20: 215-221.

[113]

Cannon DE, Frank DO, Kimbell JS, Poetker DM, Rhee JS. Modeling nasal physiology changes due to septal perforations. Otolaryngol. Head. Neck Surg., 2013, 148: 513-518.

[114]

Farzal Z, . A computational fluid dynamics analysis of the effects of size and shape of anterior nasal septal perforations. Rhinology, 2019, 57: 153-159.

[115]

Efanov JI, Roy A-A, Huang KN, Borsuk DE. Virtual surgical planning: the pearls and pitfalls. Plast. Reconstr. Surg. Glob. Open, 2018, 6: e1443-e1443.

[116]

Chim H, Wetjen N, Mardini S. Virtual surgical planning in craniofacial surgery. Semin. Plast. Surg., 2014, 28: 150-158.

[117]

Naran S, Steinbacher DM, Taylor JA. Current concepts in orthognathic surgery. Plast. Reconstr. Surg., 2018, 141: 925e-936e.

[118]

Dawood A, Marti Marti B, Sauret-Jackson V, Darwood A. 3D printing in dentistry. Br. Dent. J., 2015, 219: 521-529.

[119]

Yao B, . Reconstruction of bilateral post-traumatic midfacial defects assisted by three-dimensional craniomaxillofacial data in normal Chinese people—a preliminary study. J. Oral Maxillofac. Surg., 2019, 77: 2302.e2301-2302.e2313.

[120]

Frank-Ito DO, Kimbell JS, Laud P, Garcia GJM, Rhee JS. Predicting postsurgery nasal physiology with computational modeling: current challenges and limitations. Otolaryngol. Head. Neck Surg., 2014, 151: 751-759.

[121]

Vanhille DL, . Virtual surgery for the nasal airway. JAMA Facial Plast. Surg., 2018, 20: 63-69.

[122]

Willaert RV, Opdenakker Y, Sun Y, Politis C, Vermeersch H. New technologies in rhinoplasty: a comprehensive workflow for computer-assisted planning and execution. Plast. Reconstr. Surg. Glob. Open, 2019, 7: e2121-e2121.

[123]

Zeng W, . The combined application of database and three-dimensional image registration technology in the restoration of total nose defect. J. Craniofac. Surg., 2018, 29: e484-e487.

[124]

Bekisz JM, . In-house manufacture of sterilizable, scaled, patient-specific 3D-printed models for rhinoplasty. Aesthetic Surg. J., 2019, 39: 254-263.

[125]

Hierl T, Arnold S, Kruber D, Schulze FP, Humpfner-Hierl H. CAD-CAM-assisted esthetic facial surgery. J. Oral. Maxillofac. Surg., 2013, 71: e15-e23.

[126]

Vanhille DL, . Virtual surgery for the nasal airway: a preliminary report on decision support and technology acceptance. JAMA Facial Plast. Surg., 2018, 20: 63-69.

[127]

Rhee JS, Cannon DE, Frank DO, Kimbell JS. Role of virtual surgery in preoperative planning: assessing the individual components of functional nasal airway surgery. Arch. Facial Plast. Surg., 2012, 14: 354-359.

[128]

Ozlugedik S, . Numerical study of the aerodynamic effects of septoplasty and partial lateral turbinectomy. Laryngoscope, 2008, 118: 330-334.

[129]

Frank-Ito DO, Kimbell JS, Borojeni AAT, Garcia GJM, Rhee JS. A hierarchical stepwise approach to evaluate nasal patency after virtual surgery for nasal airway obstruction. Clin. Biomech., 2019, 61: 172-180.

[130]

Zarrabi S, Welch M, Neary J, Kim BJ. A novel approach for total nasal reconstruction. J. Oral. Maxillofac. Surg., 2019, 77: 1073.e1071-1073.e1011.

[131]

Cutting C, Oliker A, Haring J, Dayan J, Smith D. Use of three-dimensional computer graphic animation to illustrate cleft lip and palate surgery. Comput. Aided Surg., 2002, 7: 326-331.

[132]

Kantar RS, . Knowledge and skills acquisition by plastic surgery residents through digital simulation training: a prospective, randomized, blinded trial. Plast. Reconstr. Surg., 2020, 145: 184e-192e.

[133]

Plana NM, . A prospective, randomized, blinded trial comparing digital simulation to textbook for cleft surgery education. Plast. Reconstr. Surg., 2019, 143: 202-209.

[134]

Plana NM, Diaz-Siso JR, Culnan DM, Cutting CB, Flores RL. The first year of global cleft surgery education through digital simulation: a proof of concept. Cleft Palate Craniofac. J., 2018, 55: 626-629.

[135]

Prendergast, M. E. & Burdick, J. A. Recent advances in enabling technologies in 3D printing for precision medicine. Adv. Mater. (Deerfield Beach, Fla.) e1902516 (2019).

[136]

Wee JH, Park MH, Oh S, Jin HR. Complications associated with autologous rib cartilage use in rhinoplasty: a meta-analysis. JAMA Facial Plast. Surg., 2015, 17: 49-55.

[137]

Pirsig W, Kern EB, Verse T. Reconstruction of anterior nasal septum: back-to-back autogenous ear cartilage graft. Laryngoscope, 2004, 114: 627-638.

[138]

Gurlek A, . The use of high-density porous polyethylene as a custom-made nasal spreader graft. Aesthetic Plast. Surg., 2006, 30: 34-41.

[139]

Patel K, Brandstetter K. Solid implants in facial plastic surgery: potential complications and how to prevent them. Facial Plast. Surg., 2016, 32: 520-531.

[140]

Zhong N, Zhao X. 3D printing for clinical application in otorhinolaryngology. Eur. Arch. Otorhinolaryngol., 2017, 274: 4079-4089.

[141]

Yi, H.-G. et al. Three-dimensional printing of a patient-specific engineered nasal cartilage for augmentative rhinoplasty. J. Tissue Eng. 10, 2041731418824797 (2019).

[142]

Tao, O. et al. The applications of 3D printing for craniofacial tissue engineering. Micromachines 10, https://doi.org/10.3390/mi10070480 (2019).

[143]

Mandrycky C, Wang Z, Kim K, Kim D-H. 3D bioprinting for engineering complex tissues. Biotechnol. Adv., 2016, 34: 422-434.

[144]

Tao O, . The applications of 3D printing for craniofacial tissue engineering. Micromachines, 2019, 10: 480.

[145]

Du Y, Guo JL, Wang J, Mikos AG, Zhang S. Hierarchically designed bone scaffolds: from internal cues to external stimuli. Biomaterials, 2019, 218: 119334.

[146]

Kushnaryov A, . Evaluation of autogenous engineered septal cartilage grafts in rabbits: a minimally invasive preclinical model. Adv. Otolaryngol., 2014, 2014: 7.

[147]

Pleumeekers MM, . Cartilage regeneration in the head and neck area: combination of ear or nasal chondrocytes and mesenchymal stem cells improves cartilage production. Plast. Reconstr. Surg., 2015, 136: 762e-774e.

[148]

Oseni AO, Butler PE, Seifalian AM. Optimization of chondrocyte isolation and characterization for large-scale cartilage tissue engineering. J. Surg. Res., 2013, 181: 41-48.

[149]

Schwarz S, . Processed xenogenic cartilage as innovative biomatrix for cartilage tissue engineering: effects on chondrocyte differentiation and function. J. Tissue Eng. Regen. Med., 2015, 9: E239-E251.

[150]

Mendelson A, Ahn JM, Paluch K, Embree MC, Mao JJ. Engineered nasal cartilage by cell homing: a model for augmentative and reconstructive rhinoplasty. Plast. Reconstr. Surg., 2014, 133: 1344-1353.

[151]

Kundu J, Shim J-H, Jang J, Kim S-W, Cho D-W. An additive manufacturing-based PCL–alginate–chondrocyte bioprinted scaffold for cartilage tissue engineering. J. Tissue Eng. Regen. Med., 2015, 9: 1286-1297.

[152]

Graham ME, Gratzer PF, Bezuhly M, Hong P. Development and characterization of decellularized human nasoseptal cartilage matrix for use in tissue engineering. Laryngoscope, 2016, 126: 2226-2231.

[153]

Fulco I, . Engineered autologous cartilage tissue for nasal reconstruction after tumour resection: an observational first-in-human trial. Lancet, 2014, 384: 337-346.

[154]

Chiu LLY, To WTH, Lee JM, Waldman SD. Scaffold-free cartilage tissue engineering with a small population of human nasoseptal chondrocytes. Laryngoscope, 2017, 127: E91-E99.

[155]

Watson D, . Effect of hyaluronidase on tissue-engineered human septal cartilage. Laryngoscope, 2016, 126: 1984-1989.

[156]

Tee CA, . Improved zonal chondrocyte production protocol integrating size-based inertial spiral microchannel separation and dynamic microcarrier culture for clinical application. Biomaterials, 2019, 220: 119409.

[157]

Correia C, . Dynamic culturing of cartilage tissue: the significance of hydrostatic pressure. Tissue Eng. Part A, 2012, 18: 1979-1991.

[158]

Wiggenhauser PS, Balmayor ER, Rotter N, Schantz JT. In vivo evaluation of a regenerative approach to nasal dorsum augmentation with a polycaprolactone-based implant. Eur. J. Med. Res., 2019, 24

[159]

Park SH, . New application of three-dimensional printing biomaterial in nasal reconstruction. Laryngoscope, 2017, 127: 1036-1043.

[160]

Kim YS, . The application of three-dimensional printing in animal model of augmentation rhinoplasty. Ann. Biomed. Eng., 2015, 43: 2153-2162.

[161]

Unkovskiy A, Spintzyk S, Brom J, Huettig F, Keutel C. Direct 3D printing of silicone facial prostheses: a preliminary experience in digital workflow. J. Prosthet. Dent., 2018, 120: 303-308.

[162]

Nuseir A, . Direct 3D printing of flexible nasal prosthesis: optimized digital workflow from scan to fit. J. Prosthodont., 2019, 28: 10-14.

[163]

Stokken JK, Pallanch JF. The emerging role of 3-dimensional printing in rhinology. Otolaryngol. Clin. N. Am., 2017, 50: 583-588.

[164]

Kim DH, . Clinical application of 3-dimensional printing technology for patients with nasal septal deformities: a multicenter study. JAMA Otolaryngol. Head Neck Surg., 2018, 144: 1145-1152.

[165]

Qassemyar Q, Assouly N, Madar Y, Temam S, Kolb F. Total nasal reconstruction with 3D custom made porous titanium prosthesis and free thoracodorsal artery perforator flap: a case report. Microsurgery, 2018, 38: 567-571.

[166]

Khan G, Choi YS, Park ES, Choi YD. The application of three-dimensional simulation program and three-dimensional printing in secondary rhinoplasty. J. Craniofac. Surg., 2018, 29: e774-e777.

[167]

Choi YD, Kim Y, Park E. Patient-specific augmentation rhinoplasty using a three-dimensional simulation program and three-dimensional printing. Aesthetic Surg. J., 2017, 37: 988-998.

[168]

Yi H-G, . Three-dimensional printing of a patient-specific engineered nasal cartilage for augmentative rhinoplasty. J. Tissue Eng., 2019, 10: 2041731418824797.

AI Summary AI Mindmap
PDF

212

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/