Skip to main content

Fundamentals of Navigation Surgery

  • Chapter
  • First Online:
Navigation in Oral and Maxillofacial Surgery

Abstract

What a surgeon needs in an operating room is a sense of confidence to perform a safe and less invasive surgery by avoiding critical anatomies and preferably on a preplanned map. Advanced technology has become an aid in bringing the preoperative images and surgery plans aligned with the surgical tools navigated into the operating room in a real-time scenario. This technology has led to the emerging of the image-guided surgery (IGS) systems designed for the benefit of surgeons and patients and nowadays has become essential in performing most surgeries. The main aim of IGS is to enable surgeons to precisely localize the surgery regions by accurate positioning of the surgical tools over the preoperative images of the patient. In order to have this scenario happen precisely in the operating room, there have been engagements of lots of innovative technologies and implemented algorithms into an image-guided system. This book chapter is devoted to briefly discuss the facts behind IGS technology from the beginning until now and highlight some critical challenges that affect the system’s performance. The authors of this chapter have been carrying out extensive works and original researches on the design, implementation, and improvement of the image-guided navigation systems in almost all surgical disciplines, including neurosurgery, ears, nose, and throat, and craniomaxillofacial (CMF), spine, for more than 10 years. Therefore, the main aim of this chapter is to share our extensive experiences and knowledge in this field with people from technical and medical staff who are enthusiastically promoting new emerging technologies into the operating rooms for better care of patients.

We first introduce the main components of an image-guided system, then go through the most critical and challenging part of IGS, called registration procedures. Next, some advanced registration algorithms and their impact on the accuracy of navigation systems with their applications in different surgeries with a focus on CMF have been discussed. Finally, we have drawn some statements on the current challenges in IGS, such as tissue deformation, with some solutions and the future aspects of this technology in medicine.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.medgadget.com/2020/03/surgical-navigation-systems-market-to-reach-usd-1-21-billion-by-2026.html

  2. 2.

    https://www.alliedmarketresearch.com/surgical-navigation-systems-market

  3. 3.

    https://www.fda.gov/radiation-emitting-products/medical-x-ray-imaging/dental-cone-beam-computed-tomography

References

  1. Citardi MJ. In: Labadie R, Fitzpatrick JM, editors. Image-guided surgery: fundamentals and clinical applications in otolaryngology. San Diego, CA: Plural Publishing; 2016, 215 pp.

    Google Scholar 

  2. Wang JC, Nagy L, Demke JC. Image-guided surgery and craniofacial applications: mastering the unseen. Maxillofac Plast Reconst Surg. 2015;37(1):1–5.

    Google Scholar 

  3. Bessen SY, Wu X, Sramek MT, Shi Y, Pastel D, Halter R, et al. Image-guided surgery in otolaryngology: a review of current applications and future directions in head and neck surgery. Head Neck. 2021;43(8):2534–53.

    Article  Google Scholar 

  4. Schmale IL, Vandelaar LJ, Luong AU, Citardi MJ, Yao WC. Image-guided surgery and intraoperative imaging in rhinology: clinical update and current state of the art. Ear Nose Throat J. 2021;100(10):NP475–86.

    Article  Google Scholar 

  5. García-Mato D, Ochandiano S, García-Sevilla M, Navarro-Cuéllar C, Darriba-Allés JV, García-Leal R, et al. Craniosynostosis surgery: workflow based on virtual surgical planning, intraoperative navigation and 3D printed patient-specific guides and templates. Sci Rep. 2019;9(1):1–10.

    Article  Google Scholar 

  6. Kaduk W, Podmelle F, Louis PJ. Surgical navigation in reconstruction. Oral Maxillofac Surg Clin North Am. 2013;25(2):313–33.

    Article  Google Scholar 

  7. Luz M, Strauss G, Manzey D. Impact of image-guided surgery on surgeons’ performance: a literature review. Int J Hum Factors Ergon. 2016;4(3–4):229–63.

    Article  Google Scholar 

  8. Silva D, Belsuzarri T, Barnett GH. Image-guided surgery for meningioma. In: Handbook of clinical neurology, vol. 170. Elsevier; 2020. p. 201–7.

    Google Scholar 

  9. Eggers G, Mühling J, Hofele C. Clinical use of navigation based on cone-beam computer tomography in maxillofacial surgery. Br J Oral Maxillofac Surg. 2009;47(6):450–4.

    Article  Google Scholar 

  10. Anne Woloshyn T. Soaking up the rays: Light therapy and visual culture in Britain, c. 1890–1940. Manchester: Manchester University Press; 2017.

    Book  Google Scholar 

  11. Kyriakides Y. Accuracy assessment of a novel optical image guided system for trans-nasal sinus and skull base surgeries. Int Bull Otorhinolaryngol. 2020;16(2):41–5.

    Article  Google Scholar 

  12. Rahman M, Murad GJ, Mocco J. Early history of the stereotactic apparatus in neurosurgery. Neurosurg Focus. 2009;27(3):E12.

    Article  Google Scholar 

  13. Jensen RL, Stone JL, Hayne RA. Introduction of the human Horsley-Clarke stereotactic frame. Neurosurgery. 1996;38(3):563–7.

    CAS  Google Scholar 

  14. Henderson JM, Holloway KL, Gaede SE, Rosenow JM. The application accuracy of a skull-mounted trajectory guide system for image-guided functional neurosurgery. Comput Aided Surg. 2004;9(4):155–60.

    Article  Google Scholar 

  15. Vogele M, Freysinger W, Bale R, Gunkel A, Thumfart W. Einsatz der ISG Viewing Wand am Felsenbein Eine Modellstudie. HNO. 1997;45(2):74–80.

    Article  CAS  Google Scholar 

  16. Haßfeld S, Mühling J, Zöller J. Intraoperative navigation in oral and maxillofacial surgery. Int J Oral Maxillofac Surg. 1995;24(1):111–9.

    Article  Google Scholar 

  17. Bell RB. Computer planning and intraoperative navigation in cranio-maxillofacial surgery. Oral Maxillofac Surg Clin. 2010;22(1):135–56.

    Article  Google Scholar 

  18. Hammer B, Kunz C, Schramm A, Prein J. Repair of complex orbital fractures: technical problems, state-of-the-art solutions and future perspectives. Ann Acad Med Singap. 1999;28(5):687–91.

    CAS  Google Scholar 

  19. Marmulla R, Niederdellmann H. Computer-assisted bone segment navigation. J Cranio-Maxillofac Surg. 1998;26(6):347–59.

    Article  CAS  Google Scholar 

  20. Tatli U, Evlice B. Cone-beam computed tomography for oral and maxillofacial imaging. In: Computed tomography: advanced applications. Rijeka: InTech; 2017. p. 139.

    Google Scholar 

  21. Zhao M, Wang L, Chen J, Nie D, Cong Y, Ahmad S, et al., editors. Craniomaxillofacial bony structures segmentation from MRI with deep-supervision adversarial learning. International conference on medical image computing and computer-assisted intervention. Springer;2018.

    Google Scholar 

  22. Scarfe WC, Li Z, Aboelmaaty W, Scott S, Farman A. Maxillofacial cone beam computed tomography: essence, elements and steps to interpretation. Aust Dent J. 2012;57:46–60.

    Article  Google Scholar 

  23. Lee Y-H, Lee KM, Auh Q. MRI-based assessment of masticatory muscle changes in TMD patients after whiplash injury. J Clin Med. 2021;10(7):1404.

    Article  Google Scholar 

  24. Yilmaz HH, Yildirim D, Ugan Y, Tunc SE, Yesildag A, Orhan H, et al. Clinical and magnetic resonance imaging findings of the temporomandibular joint and masticatory muscles in patients with rheumatoid arthritis. Rheumatol Int. 2012;32(5):1171–8.

    Article  Google Scholar 

  25. Karumuri SK, Rastogi T, Beeraka K, Penumatcha MR, Olepu SR. Ultrasound: a revenant therapeutic modality in dentistry. J Clin Diagn Res. 2016;10(7):ZE08.

    Google Scholar 

  26. Slak B, Daabous A, Bednarz W, Strumban E, Maev RG. Assessment of gingival thickness using an ultrasonic dental system prototype: a comparison to traditional methods. Ann Anat. 2015;199:98–103.

    Article  Google Scholar 

  27. Gsaxner C, Wallner J, Chen X, Zemann W, Egger J. Facial model collection for medical augmented reality in oncologic cranio-maxillofacial surgery. Sci Data. 2019;6(1):1–7.

    Article  Google Scholar 

  28. Nardi C, Molteni R, Lorini C, Taliani GG, Matteuzzi B, Mazzoni E, et al. Motion artefacts in cone beam CT: an in vitro study about the effects on the images. Br J Radiol. 2016;89(1058):20150687.

    Article  Google Scholar 

  29. Nardi C, Borri C, Regini F, Calistri L, Castellani A, Lorini C, et al. Metal and motion artifacts by cone beam computed tomography (CBCT) in dental and maxillofacial study. Radiol Med. 2015;120(7):618–26.

    Article  Google Scholar 

  30. Schulze R, Heil U, Groβ D, Bruellmann D, Dranischnikow E, Schwanecke U, et al. Artefacts in CBCT: a review. Dentomaxillofac Radiol. 2011;40(5):265–73.

    Article  CAS  Google Scholar 

  31. Bhoosreddy AR, Sakhavalkar PU. Image deteriorating factors in cone beam computed tomography, their classification, and measures to reduce them: a pictorial essay. J Indian Acad Oral Med Radiol. 2014;26(3):293.

    Article  Google Scholar 

  32. Makins SR. Artifacts interfering with interpretation of cone beam computed tomography images. Dent Clin N Am. 2014;58(3):485–95.

    Article  Google Scholar 

  33. Barrett JF, Keat N. Artifacts in CT: recognition and avoidance. Radiographics. 2004;24(6):1679–91.

    Article  Google Scholar 

  34. Fox A, Basrani B, Kishen A, Lam EW. A novel method for characterizing beam hardening artifacts in cone-beam computed tomographic images. J Endod. 2018;44(5):869–74.

    Article  Google Scholar 

  35. Nagarajappa AK, Dwivedi N, Tiwari R. Artifacts: the downturn of CBCT image. J Int Soc Prev Community Dent. 2015;5(6):440.

    Article  Google Scholar 

  36. Hong J, Hashizume M. An effective point-based registration tool for surgical navigation. Surg Endosc. 2010;24(4):944–8.

    Article  Google Scholar 

  37. Zhou Z, Wu B, Duan J, Zhang X, Zhang N, Liang Z. Optical surgical instrument tracking system based on the principle of stereo vision. J Biomed Opt. 2017;22(6):065005.

    Article  Google Scholar 

  38. Sorriento A, Porfido MB, Mazzoleni S, Calvosa G, Tenucci M, Ciuti G, et al. Optical and electromagnetic tracking systems for biomedical applications: a critical review on potentialities and limitations. IEEE Rev Biomed Eng. 2019;13:212–32.

    Article  Google Scholar 

  39. Kügler D, Krumb H, Bredemann J, Stenin I, Kristin J, Klenzner T, et al. High-precision evaluation of electromagnetic tracking. Int J Comput Assist Radiol Surg. 2019;14(7):1127–35.

    Article  Google Scholar 

  40. Burström G, Nachabe R, Homan R, Hoppenbrouwers J, Holthuizen R, Persson O, et al. Frameless patient tracking with adhesive optical skin markers for augmented reality surgical navigation in spine surgery. Spine. 2020;45(22):1598–604.

    Article  Google Scholar 

  41. Drouin S, Kochanowska A, Kersten-Oertel M, Gerard IJ, Zelmann R, De Nigris D, et al. IBIS: an OR ready open-source platform for image-guided neurosurgery. Int J Comput Assist Radiol Surg. 2017;12(3):363–78.

    Article  Google Scholar 

  42. Ewurum CH, Guo Y, Pagnha S, Feng Z, Luo X. Surgical navigation in orthopedics: workflow and system review. Int Orthop. 2018;1093:47–63.

    Google Scholar 

  43. Galletti B, Gazia F, Freni F, Sireci F, Galletti F. Endoscopic sinus surgery with and without computer assisted navigation: a retrospective study. Auris Nasus Larynx. 2019;46(4):520–5.

    Article  Google Scholar 

  44. Mediavilla Guzmán A, Riad Deglow E, Zubizarreta-Macho Á, Agustín-Panadero R, Hernández MS. Accuracy of computer-aided dynamic navigation compared to computer-aided static navigation for dental implant placement: an in vitro study. J Clin Med. 2019;8(12):2123.

    Article  Google Scholar 

  45. Majak M, Zuk M, Swiatek-Najwer E, Popek M, Pietruski P, editors. Biopsy procedure applied in MentorEye molecular surgical navigation system. European congress on computational methods in applied sciences and engineering. Springer; 2017.

    Google Scholar 

  46. Pelanis E, Teatini A, Eigl B, Regensburger A, Alzaga A, Kumar RP, et al. Evaluation of a novel navigation platform for laparoscopic liver surgery with organ deformation compensation using injected fiducials. Med Image Anal. 2021;69:101946.

    Article  Google Scholar 

  47. Sukegawa S, Kanno T, Furuki Y. Application of computer-assisted navigation systems in oral and maxillofacial surgery. Jpn Dent Sci Rev. 2018;54(3):139–49.

    Article  Google Scholar 

  48. Kurozumi K, Kameda M, Ishida J, Date I. Simultaneous combination of electromagnetic navigation with visual evoked potential in endoscopic transsphenoidal surgery: clinical experience and technical considerations. Acta Neurochir. 2017;159(6):1043.

    Article  Google Scholar 

  49. Lavasani SN, Farnia P, Najafzadeh E, Saghatchi S, Samavati M, Abtahi H, et al. Bronchoscope motion tracking using centerline-guided Gaussian mixture model in navigated bronchoscopy. Phys Med Biol. 2021;66(2):025001.

    Article  Google Scholar 

  50. Rania A, May I, Othman M. Evaluation of surgical-navigation system in management of orbital disorders. Med J Cairo Univ. 2019;87(June):1349–55.

    Article  Google Scholar 

  51. Keeble H, Lavrador JP, Pereira N, Lente K, Brogna C, Gullan R, et al. Electromagnetic navigation systems and intraoperative neuromonitoring: reliability and feasibility study. Oper Neurosurg (Hagerstown). 2021;20(4):373–82.

    Article  Google Scholar 

  52. Jaeger HA, Nardelli P, O'shea C, Tugwell J, Khan KA, Power T, et al. Automated catheter navigation with electromagnetic image guidance. IEEE Trans Biomed Eng. 2017;64(8):1972–9.

    Article  Google Scholar 

  53. Maier J, Weiherer M, Huber M, Palm C. Optically tracked and 3D printed haptic phantom hand for surgical training system. Quant Imaging Med Surg. 2020;10(2):340.

    Article  Google Scholar 

  54. Maier-Hein L, Franz A, Meinzer H-P, Wolf I, editors. Comparative assessment of optical tracking systems for soft tissue navigation with fiducial needles. Medical imaging 2008: visualization, image-guided procedures, and modeling. International Society for Optics and Photonics; 2008.

    Google Scholar 

  55. Marinetto E, Garcia-Mato D, Garcia A, Martinez S, Desco M, Pascau J. Multicamera optical tracker assessment for computer aided surgery applications. IEEE Access. 2018;6:64359–70.

    Article  Google Scholar 

  56. Preim B, Botha CP. Visual computing for medicine: theory, algorithms, and applications. Boston: Newnes; 2013.

    Google Scholar 

  57. Attivissimo F, Lanzolla AML, Carlone S, Larizza P, Brunetti G. A novel electromagnetic tracking system for surgery navigation. Comput Assist Surg. 2018;23(1):42–52.

    Article  Google Scholar 

  58. Navaei Lavasani S, Deevband M, Farnia P, Ahmadian A, Saghatchi S. Compensation of dynamic electromagnetic field distortion using simultaneous localization and mapping method with application in endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA) guidance. Int J Med Robot. 2020;16(1):e2035.

    Article  Google Scholar 

  59. Sadjadi H, Hashtrudi-Zaad K, Fichtinger G. Simultaneous localization and calibration for electromagnetic tracking systems. Int J Med Robot. 2016;12(2):189–98.

    Article  Google Scholar 

  60. Wang J, Zhang W, Chen M. A survey of 3D image navigation and high precision dynamic registration in minimally invasive surgery. Procedia Comput Sci. 2018;131:320–6.

    Article  CAS  Google Scholar 

  61. Maintz JA, Viergever MA. A survey of medical image registration. Med Image Anal. 1998;2(1):1–36.

    Article  CAS  Google Scholar 

  62. Al-Azzawi N, Abdullah WAKW. MRI monomodal feature-based registration based on the efficiency of multiresolution representation and mutual information. Am J Biomed Eng. 2012;2(3):98–104.

    Article  Google Scholar 

  63. Toth D, Miao S, Kurzendorfer T, Rinaldi CA, Liao R, Mansi T, et al. 3D/2D model-to-image registration by imitation learning for cardiac procedures. Int J Comput Assist Radiol Surg. 2018;13(8):1141–9.

    Article  Google Scholar 

  64. Farnia P, Makkiabadi B, Ahmadian A, Alirezaie J, editors. Curvelet based residual complexity objective function for non-rigid registration of pre-operative MRI with intra-operative ultrasound images. 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2016.

    Google Scholar 

  65. Ahmadian A, Fathi Kazerooni A, Mohagheghi S, Amini Khoiy K, Sadr Hosseini M. A region-based anatomical landmark configuration for sinus surgery using image guided navigation system: a phantom-study. J Cranio-Maxillofac Surg. 2014;42(6):816–24.

    Article  Google Scholar 

  66. Farnia P, Najafzadeh E, Ahmadian A, Makkiabadi B, Alimohamadi M, Alirezaie J, editors. Co-sparse analysis model based image registration to compensate brain shift by using intra-operative ultrasound imaging. 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2018.

    Google Scholar 

  67. Besl PJ, ND MK, editors. Method for registration of 3-D shapes. Sensor fusion IV: control paradigms and data structures. International Society for Optics and Photonics; 1992.

    Google Scholar 

  68. Farnia P, Ahmadian A, Khoshnevisan A, Jaberzadeh A, Serej ND, Kazerooni AF, editors. An efficient point based registration of intra-operative ultrasound images with MR images for computation of brain shift; a phantom study. 2011 annual international conference of the IEEE engineering in medicine and biology society. IEEE; 2011.

    Google Scholar 

  69. Noori SMR, Farnia P, Bayat M, Bahrami N, Shakourirad A, Ahmadian A. Automatic detection of symmetry plane for computer-aided surgical simulation in craniomaxillofacial surgery. Phys Eng Sci Med. 2020;43(3):1087–99.

    Article  Google Scholar 

  70. Saghatchi S, Sadeghi MJ, Ahmadian A, Farahmand F, Sarkar S. Navigating an imaging instrument in a branched structure. Google Patents; 2018.

    Google Scholar 

  71. Ershad M, Ahmadian A, Dadashi Serej N, Saberi H, Amini KK. Minimization of target registration error for vertebra in image-guided spine surgery. Int J Comput Assist Radiol Surg. 2014;9(1):29–38.

    Article  Google Scholar 

  72. Mohagheghi S, Ahmadian A, Yaghoobee S. Accuracy assessment of a marker-free method for registration of CT and stereo images applied in image-guided implantology: a phantom study. J Craniomaxillofac Surg. 2014;42(8):1977–84.

    Article  Google Scholar 

  73. Lin Q, Cai K, Yang R, Xiao W, Huang J, Zhan Y, et al. Geometric calibration of markerless optical surgical navigation system. Int J Med Robot. 2019;15(2):e1978.

    Article  Google Scholar 

  74. Serej ND, Ahmadian A, Mohagheghi S, Sadrehosseini SM. A projected landmark method for reduction of registration error in image-guided surgery systems. Int J Comput Assist Radiol Surg. 2015;10(5):541–54.

    Article  Google Scholar 

  75. Raabe A, Krishnan R, Wolff R, Hermann E, Zimmermann M, Seifert V. Laser surface scanning for patient registration in intracranial image-guided surgery. Neurosurgery. 2002;50(4):797–803.

    Article  Google Scholar 

  76. Wang D, Ma D, Wong ML, Wáng YXJ. Recent advances in surgical planning & navigation for tumor biopsy and resection. Quant Imaging Med Surg. 2015;5(5):640.

    Google Scholar 

  77. Knoops PG, Beaumont CA, Borghi A, Rodriguez-Florez N, Breakey RW, Rodgers W, et al. Comparison of three-dimensional scanner systems for craniomaxillofacial imaging. J Plast Reconstr Aesthet Surg. 2017;70(4):441–9.

    Article  Google Scholar 

  78. Zheng Y, Doermann D. Robust point matching for nonrigid shapes by preserving local neighborhood structures. IEEE Trans Pattern Anal Mach Intell. 2006;28(4):643–9.

    Article  Google Scholar 

  79. Jian B, Vemuri BC. Robust point set registration using gaussian mixture models. IEEE Trans Pattern Anal Mach Intell. 2010;33(8):1633–45.

    Article  Google Scholar 

  80. Myronenko A, Song X. Point set registration: coherent point drift. IEEE Trans Pattern Anal Mach Intell. 2010;32(12):2262–75.

    Article  Google Scholar 

  81. Mohammadi A, Ahmadian A, Rabbani S, Fattahi E, Shirani S. A combined registration and finite element analysis method for fast estimation of intraoperative brain shift; phantom and animal model study. Int J Med Robot. 2017;13(4):e1792.

    Article  Google Scholar 

  82. Mohammadi A, Ahmadian A, Azar AD, Sheykh AD, Amiri F, Alirezaie J. Estimation of intraoperative brain shift by combination of stereovision and doppler ultrasound: phantom and animal model study. Int J Comput Assist Radiol Surg. 2015;10(11):1753–64.

    Article  Google Scholar 

  83. Gerard IJ, Kersten-Oertel M, Petrecca K, Sirhan D, Hall JA, Collins DL. Brain shift in neuronavigation of brain tumors: a review. Med Image Anal. 2017;35:403–20.

    Article  Google Scholar 

  84. Bayer S, Maier A, Ostermeier M, Fahrig R. Intraoperative imaging modalities and compensation for brain shift in tumor resection surgery. Int J Biomed Imaging. 2017;2017:6028645.

    Article  Google Scholar 

  85. Kuhnt D, Bauer MH, Nimsky C. Brain shift compensation and neurosurgical image fusion using intraoperative MRI: current status and future challenges. Crit Rev Biomed Eng. 2012;40(3):175–85.

    Article  Google Scholar 

  86. Farnia P, Ahmadian A, Shabanian T, Serej ND, Alirezaie J. Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity. Int J Comput Assist Radiol Surg. 2015;10(5):555–62.

    Article  CAS  Google Scholar 

  87. Stummer W, Molina ES. Fluorescence imaging/agents in tumor resection. Neurosurg Clin. 2017;28(4):569–83.

    Article  Google Scholar 

  88. Mehrmohammadi M, Joon Yoon S, Yeager D, Emelianov Y, S. Photoacoustic imaging for cancer detection and staging. Curr Mol Imaging. 2013;2(1):89–105.

    Article  CAS  Google Scholar 

  89. Najafzadeh E, Ghadiri H, Alimohamadi M, Farnia P, Mehrmohammadi M, Ahmadian A. Application of multi-wavelength technique for photoacoustic imaging to delineate tumor margins during maximum-safe resection of glioma: a preliminary simulation study. J Clin Neurosci. 2019;70:242–6.

    Article  CAS  Google Scholar 

  90. Najafzadeh E, Ghadiri H, Alimohamadi M, Farnia P, Mehrmohammadi M, Ahmadian A. Evaluation of multi-wavelengths LED-based photoacoustic imaging for maximum safe resection of glioma: a proof of concept study. Int J Comput Assist Radiol Surg. 2020;15:1053–62.

    Article  CAS  Google Scholar 

  91. Arabpou S, Najafzadeh E, Farnia P, Ahmadian A, Ghadiri H, Akhoundi MSA. Detection of early stages dental caries using photoacoustic signals: the simulation study. Front Biomed Technol. 2019.

    Google Scholar 

  92. Jokerst J, Moore C, Hariri A. Photoacoustic imaging for noninvasive periodontal probing depth measurements. Google Patents; 2021.

    Google Scholar 

  93. Yan Y, John S, Ghalehnovi M, Kabbani L, Kennedy NA, Mehrmohammadi M. Photoacoustic imaging for image-guided endovenous laser ablation procedures. Sci Rep. 2019;9(1):1–10.

    Article  Google Scholar 

  94. Eddins B, Bell MAL. Design of a multifiber light delivery system for photoacoustic-guided surgery. J Biomed Opt. 2017;22(4):041011.

    Article  Google Scholar 

  95. Wang LV. Photoacoustic imaging and spectroscopy. CRC press; 2017.

    Book  Google Scholar 

  96. Najafzadeh E, Farnia P, Ahmadian A, Ghadiri H. Light-emitting diode based photoacoustic imaging system. Front Biomed Technol. 2020;7(3):200–4.

    Google Scholar 

  97. Beard P. Biomedical photoacoustic imaging. Interface focus. 2011;1(4):602–31.

    Article  Google Scholar 

  98. Zackrisson S, Van De Ven S, Gambhir S. Light in and sound out: emerging translational strategies for photoacoustic imaging. Cancer Res. 2014;74(4):979–1004.

    Article  CAS  Google Scholar 

  99. Farnia P, Mohammadi M, Najafzadeh E, Alimohamadi M, Makkiabadi B, Ahmadian AJBP, et al. High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging. Biomed Phys Eng Express. 2020;6(4):045019.

    Article  Google Scholar 

  100. Farnia P, Makkiabadi B, Alimohamadi M, Najafzadeh E, Basij M, Yan Y, et al. Photoacoustic-MR image registration based on a co-sparse analysis model to compensate for brain shift. Sensors (Basel, Switzerland) 2022;22(6).

    Google Scholar 

  101. Farnia P, Najafzadeh E, Hariri A, Lavasani SN, Makkiabadi B, Ahmadian A, et al. Dictionary learning technique enhances signal in LED-based photoacoustic imaging. Biomed Opt Express. 2020;11(5):2533–47.

    Article  Google Scholar 

  102. Najafzadeh E, Farnia P, Lavasani SN, Basij M, Yan Y, Ghadiri H, et al. Photoacoustic image improvement based on a combination of sparse coding and filtering. J Biomed Opt. 2020;25(10):106001.

    Article  Google Scholar 

  103. Hasan W, Daly MJ, Chan HHL, Qiu J, Irish JCJTL. Intraoperative cone-beam CT-guided osteotomy navigation in mandible and maxilla surgery. Laryngoscope. 2020;130(5):1166–72.

    Article  Google Scholar 

  104. Marescaux J, Diana M. Next step in minimally invasive surgery: hybrid image-guided surgery. J Pediatr Surg. 2015;50(1):30–6.

    Article  Google Scholar 

  105. Cutolo F. Augmented reality in image-guided surgery. 2019.

    Google Scholar 

  106. Lungu AJ, Swinkels W, Claesen L, Tu P, Egger J, Chen X. A review on the applications of virtual reality, augmented reality and mixed reality in surgical simulation: an extension to different kinds of surgery. Expert Rev Med Devices. 2021;18(1):47–62.

    Article  CAS  Google Scholar 

  107. McKnight RR, Pean CA, Buck JS, Hwang JS, Hsu JR, Pierrie SN. Virtual reality and augmented reality—translating surgical training into surgical technique. Curr Rev Musculoskelet Med. 2020;13:663–74.

    Article  Google Scholar 

  108. Liu K, Gao Y, Abdelrehem A, Zhang L, Chen X, Xie L, et al. Augmented reality navigation method for recontouring surgery of craniofacial fibrous dysplasia. Sci Rep. 2021;11(1):1–7.

    Google Scholar 

  109. Aziz MJ, Zade AAT, Farnia P, Alimohamadi M, Makkiabadi B, Ahmadian A, Alirezaie J. Accurate automatic glioma segmentation in brain MRI images based on CapsNet. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC): IEEE; 2021. p. 3882–5.

    Google Scholar 

  110. Gholizadeh-Ansari M, Alirezaie J, Babyn P. Deep learning for low-dose CT denoising using perceptual loss and edge detection layer. J Digit Imaging. 2020;33:504–15.

    Article  Google Scholar 

  111. Rivas-Blanco I, Pérez-Del-Pulgar CJ, García-Morales I, Muñoz VF. A review on deep learning in minimally invasive surgery. IEEE Access. 2021;9:48658–78.

    Article  Google Scholar 

  112. Diana M, Marescaux J. Robotic surgery. J Br Surg. 2015;102(2):e15–28.

    Article  CAS  Google Scholar 

  113. Gimenez ME. Percutaneous image-guided surgery. Int J Gastrointest Intervent. 2019;8(1):2–5.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alireza Ahmadian .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ahmadian, A., Farnia, P., Najafzadeh, E., Lavasani, S.N., Aziz, M.J., Ahmadian, A. (2022). Fundamentals of Navigation Surgery. In: Parhiz, S.A., James, J.N., Ghasemi, S., Amirzade-Iranaq, M.H. (eds) Navigation in Oral and Maxillofacial Surgery. Springer, Cham. https://doi.org/10.1007/978-3-031-06223-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06223-0_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06222-3

  • Online ISBN: 978-3-031-06223-0

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics