Surgical planning tool for robotically assisted hearing aid implantation
- 644 Downloads
For the facilitation of minimally invasive robotically performed direct cochlea access (DCA) procedure, a surgical planning tool which enables the surgeon to define landmarks for patient-to-image registration, identify the necessary anatomical structures and define a safe DCA trajectory using patient image data (typically computed tomography (CT) or cone beam CT) is required. To this end, a dedicated end-to-end software planning system for the planning of DCA procedures that addresses current deficiencies has been developed.
Efficient and robust anatomical segmentation is achieved through the implementation of semiautomatic algorithms; high-accuracy patient-to-image registration is achieved via an automated model-based fiducial detection algorithm and functionality for the interactive definition of a safe drilling trajectory based on case-specific drill positioning uncertainty calculations was developed.
The accuracy and safety of the presented software tool were validated during the conduction of eight DCA procedures performed on cadaver heads. The plan for each ear was completed in less than 20 min, and no damage to vital structures occurred during the procedures. The integrated fiducial detection functionality enabled final positioning accuracies of \(0.15\pm 0.08\) mm.
Results of this study demonstrated that the proposed software system could aid in the safe planning of a DCA tunnel within an acceptable time.
KeywordsSurgical planning Hearing aid implantation Robotically assisted microsurgery Segmentation
- 2.Kronenberg J, Baumgartner W, Migirov L, Dagan T, Hildesheimer M (2004) The suprameatal approach: an alternative surgical approach to cochlear implantation. Otol Neurotol 25(1):41–44 (discussion 44–45)Google Scholar
- 5.Labadie RF, Chodhury P, Cetinkaya E, Balachandran R, Haynes DS, Fenlon MR, Jusczyzck AS, Fitzpatrick JM (2005) Minimally invasive, image-guided, facial-recess approach to the middle ear: demonstration of the concept of percutaneous cochlear access in vitro. Otol Neurotol 26(4):557–562PubMedCrossRefGoogle Scholar
- 6.Balachandran R, Mitchell JE, Blachon G, Noble JH, Dawant BM, Fitzpatrick JM, Labadie RF (2010) Percutaneous cochlear implant drilling via customized frames: an in vitro study. Otolaryngol Head Neck Surg Off J Am Acad Otolaryngol Head Neck Surg 142(3):421–426Google Scholar
- 9.Klenzner T, Ngan CC, Knapp FB, Knoop H, Kromeier J, Aschendorff A, Papastathopoulos E, Raczkowsky J, Wörn H, Schipper J (2009) New strategies for high precision surgery of the temporal bone using a robotic approach for cochlear implantation. Eur Arch Otorhinolaryngol 266(7):955–960PubMedCrossRefGoogle Scholar
- 11.Noble JH, Majdani O, Labadie RF, Dawant B, Fitzpatrick JM (2010) Automatic determination of optimal linear drilling trajectories for cochlear access accounting for drill-positioning error. Int J Med Robot 6(3):281–290Google Scholar
- 12.Rodt T, Ratiu P, Becker H, Bartling S, Kacher DF, Anderson M, Jolesz F a, Kikinis R (2002) 3D visualisation of the middle ear and adjacent structures using reconstructed multi-slice CT datasets, correlating 3D images and virtual endoscopy to the 2D cross-sectional images. Neuroradiology 44(9):783–790PubMedCrossRefGoogle Scholar
- 15.Jang HG, Chung MS, Shin DS, Park SK, Cheon KS, Park HS, Park JS (2011) Segmentation and surface reconstruction of the detailed ear structures, identified in sectioned images. Anat Rec (Hoboken, NJ: 2007) 294(4):559–564Google Scholar
- 17.Ferreira A, Gentil F, Tavares JM (2012) Segmentation algorithms for ear image data towards biomechanical studies. Comput Methods Biomech Biomed Eng PP 1–17Google Scholar
- 20.Labadie RF, Shah RJ, Harris SS, Cetinkaya E, Haynes DS, Fenlon MR, Juscyzk AS, Galloway RL, Fitzpatrick JM (2004) Submillimetric target-registration error using a novel, non-invasive fiducial system for image-guided otologic surgery. Comput Aided Surg Off J Int Soc Comput Aided Surg 9(4):145–153Google Scholar
- 21.Wang MY, Maurer CR, Fitzpatrick JM, Maciunas RJ (1996) An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head. IEEE Trans Bio-med Eng 43(6):627–637Google Scholar
- 22.Gu L, Peters T (2004) 3D Automatic fiducial marker localization approach for frameless stereotactic neuro-surgery navigation morphological treatment for the detection of fiducial markers. In: MIAR 2004, vol LNCS 3150, pp 329–336Google Scholar
- 25.Maurer CR, Fitzpatrick JM, Wang MY, Maciuna RJ (1993) Estimation of localization accuracy for markers in multimodal volume images. In: Proceedings of the 15th annual international conference of the IEEE engineering in medicine and biology societ, vol Im, no 2, pp 124–125Google Scholar
- 26.Wang M, Song Z (2008) Automatic detection of fiducial marker center based on shape index and curvedness. In: MIAR 2008, vol LNCS 5128, pp 81–88Google Scholar
- 30.Kanitsar A, Fleischmann D, Wegenkittl R, Felkel P, Gröller ME (2002) CPR-curved planar reformation. In: IEEE visualization, 2002 (VIS 2002), pp 37–44Google Scholar
- 31.Bell B, Gerber N, Williamson T, Gavaghan KA, Wimmer W, Caversaccio M, Weber S (2013) In vitro accuracy evaluation of image-guided robot system for direct cochlear access. Otol Neurotol (in press)Google Scholar