Skip to main content
SpringerLink
Log in

Registration for Orthopaedic Interventions

  • Chapter
  • First Online:
Computational Radiology for Orthopaedic Interventions

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 23))

Abstract

Registration is the process of computing the transformation that relates the coordinates of corresponding points viewed in two different coordinate systems. It is one of the key components in orthopaedic navigation guidance and robotic systems. When assessing the appropriateness of a registration method for clinical use one must consider multiple factors. Among others these include, accuracy, robustness, speed, degree of automation, detrimental effects to the patient, effects on interventional workflow, and associated financial costs. In this chapter we give an overview of registration algorithms, both those available commercially and those that have only been evaluated in the laboratory setting. We introduce the models underlying the algorithms, describe the context in which they are used and assess them using the criteria described above. We show that academic research has primarily focused on improving all aspects of registration while ignoring workflow related issues. On the other hand, commercial systems have found ways of obviating the need for registration resulting in streamlined workflows that are clinically more acceptable, albeit at a cost of being sub-optimal on other criteria. While there is no optimal registration method for all settings, we do have a respectable arsenal from which to choose.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
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

Notes

  1. 1.

    In this context the term fiducial is used to denote a point used to compute the registration, be it an artificial marker or anatomical landmark.

References

  1. (2014) Class 2 device recall spine & trauma 3D 2.0. URL http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfres/res.cfm?id=125729

  2. Amiri S, Wilson DR, Masri BA, Anglin C (2014) A low-cost tracked C-arm (TC-arm) upgrade system for versatile quantitative intraoperative imaging. Int J Comput Assist Radiol Surg 9(4):695–711

    Article  Google Scholar 

  3. Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell 9(5):698–700

    Article  Google Scholar 

  4. Audenaert E, Smet B, Pattyn C, Khanduja V (2012) Imageless versus image-based registration in navigated arthroscopy of the hip: a cadaver-based assessment. J Bone Joint Surg Br 94(5):624–629

    Article  Google Scholar 

  5. Baka N, Kaptein BL, de Bruijne M, van Walsum T, Giphart JE, Niessen W, Lelieveldt BPF (2011) 2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med Image Anal 15(6):840–850

    Article  Google Scholar 

  6. Baka N, de Bruijne M, van Walsum T, Kaptein BL, Giphart JE, Schaap M, Niessen WJ, Lelieveldt BPF (2012) Statistical shape model-based femur kinematics from biplane fluoroscopy. IEEE Trans Med Imag 31(8):1573–1583

    Article  Google Scholar 

  7. Banger M, Rowe PJ, Blyth M (2013) Time analysis of MAKO RIO UKA procedures in comparision with the Oxford UKA. Bone Joint J 95-B(Supp 28):89

    Google Scholar 

  8. Barratt DC, Penney GP, Chan CSK, Slomczykowski M, Carter TJ, Edwards PJ, Hawkes DJ (2006) Self-calibrating 3D-ultrasound-based bone registration for minimally invasive orthopedic surgery. IEEE Trans Med Image 25(3):312–323

    Article  Google Scholar 

  9. Barratt DC, Chan CSK, Edwards PJ, Penney GP, Slomczykowski M, Carter TJ, Hawkes DJ (2008) Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging. Med Image Anal 12(3):358–374

    Article  Google Scholar 

  10. Beaumont E, Beaumont P, Odermat D, Fontaine I, Jansen H, Prince F (2011) Clinical validation of computer-assisted navigation in total hip arthroplasty. Adv Orthop 171783

    Google Scholar 

  11. Behrendt D, Mütze M, Steinke H, Koestler M, Josten C, Böhme J (2012) Evaluation of 2D and 3D navigation for iliosacral screw fixation. Int J Comput Assist Radiol Surg 7(2):249–255

    Article  Google Scholar 

  12. Bertelsen A, Garin-Muga A, Echeverria M, Gomez E, Borro D (2014) Distortion correction and calibration of intra-operative spine X-ray images using a constrained DLT algorithm. Comput Med Imaging Graph 38(7):558–568

    Article  Google Scholar 

  13. Besl PJ, McKay ND (1992) A method for registration of 3D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–255

    Article  Google Scholar 

  14. Bicknell RT et al (2007) Early experience with computer-assisted shoulder hemiarthroplasty for fractures of the proximal humerus: development of a novel technique and an in vitro comparison with traditional methods. J Shoulder Elbow Surg 16(3 Suppl):S117–S125

    Article  Google Scholar 

  15. Birnbaum K, Schkommodau E, Decker N, Prescher A, Klapper U, Radermacher K (2001) Computer-assisted orthopedic surgery with individual templates and comparison to conventional operation method. Spine 26(4):365–370

    Article  Google Scholar 

  16. Blanc R, Seiler C, Székely G, Nolte L, Reyes M (2012) Statistical model based shape prediction from a combination of direct observations and various surrogates: application to orthopaedic research. Med Image Anal 16(6):1156–1166

    Article  Google Scholar 

  17. van der Bom MJ, Bartels LW, Gounis MJ, Homan R, Timmer J, Viergever MA, Pluim JPW (2010) Robust initialization of 2D–3D image registration using the projection-slice theorem and phase correlation. Med Phys 37(4):1884–1892

    Article  Google Scholar 

  18. Burckhardt K, Székely G, Nötzli H, Hodler J, Gerber C (2005) Submillimeter measurement of cup migration in clinical standard radiographs. IEEE Trans Med Imag 24(5):676–688

    Article  Google Scholar 

  19. Burckhardt K, Dora C, Gerber C, Hodler J, Székely G (2006) Measuring orthopedic implant wear on standard radiographs with a precision in the 10 μm-range. Med Image Anal 10(4):520–529

    Article  Google Scholar 

  20. Chen Y, Medioni G (1992) Object modelling by registration of multiple range images. Image Vis Comput 10(3):145–155

    Article  Google Scholar 

  21. Cho Y, Moseley DJ, Siewerdsen JH, Jaffray DA (2005) Accurate technique for complete geometric calibration of cone-beam computed tomography systems. Med Phys 32(4):968–983

    Article  Google Scholar 

  22. Claus BEH (2006) Geometry calibration phantom design for 3D imaging. In: Flynn MJ, Hsieh J (eds) SPIE medical imaging: physics of medical imaging, SPIE, p 61422E

    Google Scholar 

  23. Costa F et al (2014) Economic study: a cost-effectiveness analysis of an intraoperative compared with a preoperative image-guided system in lumbar pedicle screw fixation in patients with degenerative spondylolisthesis. Spine 14(8):1790–1796

    Article  Google Scholar 

  24. Daly MJ, Siewerdsen JH, Cho YB, Jaffray DA, Irish JC (2008) Geometric calibration of a mobile C-arm for intraoperative cone-beam CT. Med Phys 35(5):2124–2136

    Article  Google Scholar 

  25. Dang H, Otake Y, Schafer S, Stayman JW, Kleinszig G, Siewerdsen JH (2012) Robust methods for automatic image-to-world registration in cone-beam CT interventional guidance. Med Phys 39(10):6484–6498

    Article  Google Scholar 

  26. Danilchenko A, Fitzpatrick JM (2011) General approach to first-order error prediction in rigid point registration. IEEE Trans Med Imag 30(3):679–693

    Article  Google Scholar 

  27. Dobbe JGG, Strackee SD, Schreurs AW, Jonges R, Carelsen B, Vroemen JC, Grimbergen CA, Streekstra GJ (2011) Computer-assisted planning and navigation for corrective distal radius osteotomy, based on pre- and intraoperative imaging. IEEE Trans Biomed Eng 58(1):182–190

    Article  Google Scholar 

  28. Dobbe JGG, Vroemen JC, Strackee SD, Streekstra GJ (2013) Corrective distal radius osteotomy: including bilateral differences in 3-D planning. Med Biol Eng Comput 51(7):791–797

    Article  Google Scholar 

  29. Dorgham OM, Laycock SD, Fisher MH (2012) GPU accelerated generation of digitally reconstructed radiographs for 2-D/3-D image registration. IEEE Trans Biomed Eng 59(9):2594–2603

    Article  Google Scholar 

  30. Eggert DW, Lorusso A, Fisher RB (1997) Estimating 3-D rigid body transformations: a comparison of four major algorithms. Mach Vis Appl 9(5/6):272–290

    Article  Google Scholar 

  31. Ershad M, Ahmadian A, Serej ND, Saberi H, Khoiy KA (2014) Minimization of target registration error for vertebra in image-guided spine surgery. Int J Comput Assist Radiol Surg 9(1):29–38

    Article  Google Scholar 

  32. Faugeras OD, Hebert M (1986) The representation, recognition, and locating of 3-D objects. Int J Rob Res 5(3):27–52

    Article  Google Scholar 

  33. Fletcher R (1987) Practical methods of optimization, 2nd edn. Wiley, New York

    Google Scholar 

  34. Fleute M, Lavallée S, Julliard R (1999) Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery. Med Image Anal 3(3):209–222

    Article  Google Scholar 

  35. Fürnstahl P, Székely G, Gerber C, Hodler J, Snedeker JG, Harders M (2012) Computer assisted reconstruction of complex proximal humerus fractures for preoperative planning. Med Image Anal 16(3):704–720

    Article  Google Scholar 

  36. Gill S, Abolmaesumi P, Fichtinger G, Boisvert J, Pichora DR, Borshneck D, Mousavi P (2012) Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine. Med Image Anal 16(3):662–674

    Article  Google Scholar 

  37. Gong RH, Özgür G, Kürklüoglu M, Lovejoy J, Yaniv Z (2013) Interactive initialization of 2D/3D rigid registration. Med Phys 20(12):121911-1–121911-14

    Google Scholar 

  38. Gonschorek O, Hauck S, Spiegl U, Weiß T, Pätzold R, Bühren V (2011) O-arm based spinal navigation and intraoperative 3D-imaging: first experiences. Eur J Trauma Emerg Surg 37(2):99–108

    Article  Google Scholar 

  39. Habets DF, Pollmann SI, Yuan X, Peters TM, Holdsworth DW (2009) Error analysis of marker-based object localization using a single-plane XRII. Med Phys 36(1):190–200

    Article  Google Scholar 

  40. Hamming NM, Daly MJ, Irish JC, Siewerdsen JH (2009) Automatic image-to-world registration based on X-ray projections in cone-beam CT guided interventions. Med Phys 36(5):1800–1812

    Google Scholar 

  41. Hananouchi T, Saito M, Koyama T, Hagio K, Murase T, Sugano N, Yoshikawa H (2009) Tailor-made surgical guide based on rapid prototyping technique for cup insertion in total hip arthroplasty. Int J Med Robot Comput Assist Surg 5(2):164–169

    Article  Google Scholar 

  42. Hartley RI, Zisserman A (2000) Multiple view geometry in computer vision. Cambridge University Press, Cambridge

    Google Scholar 

  43. Haselbacher M, Sekyra K, Mayr E, Thaler M, Nogler M (2012) A new concept of a multiple-use screw-based shape-fitting plate in total knee arthroplasty. Bone Joint J 94-B(Supp-XLIV):65

    Google Scholar 

  44. Heimann T, Meinzer H (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543–563

    Google Scholar 

  45. Hofstetter R, Slomczykowski M, Sati M, Nolte LP (1999) Fluoroscopy as an imaging means for computer-assisted surgical navigation. Comput Aided Surg 4(2):65–76

    Article  Google Scholar 

  46. Holly LT, Block O, Johnson JP (2006) Evaluation of registration techniques for spinal image guidance. J Neurosurg Spine 4(4):323–328

    Article  Google Scholar 

  47. Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am A 4(4):629–642

    Article  MathSciNet  Google Scholar 

  48. Hurvitz A, Joskowicz L (2008) Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences. Int J Comput Assist Radiol Surg 3(6):493–504

    Article  Google Scholar 

  49. Jain AK, Mustafa T, Zhou Y, Burdette C, Chirikjian GS, Fichtinger G (2005) FTRAC-a robust fluoroscope tracking fiducial. Med Phys 32(10):3185–3198

    Article  Google Scholar 

  50. Jaramaz B, Eckman K (2006) 2D/3D registration for measurement of implant alignment after total hip replacement. In: Medical image computing and computer-assisted intervention, pp 653–661

    Google Scholar 

  51. Kang X, Armand M, Otake Y, Yau WP, Cheung PYS, Hu Y, Taylor RH (2014) Robustness and accuracy of feature-based single image 2-D-3-D registration without correspondences for image-guided intervention. IEEE Trans Biomed Eng 61(1):149–161

    Article  Google Scholar 

  52. Kantelhardt SR, Bock HC, Siam L, Larsen J, Burger R, Schillinger W, Bockermann V, Rohde V, Giese A (2010) Intra-osseous ultrasound for pedicle screw positioning in the subaxial cervical spine: an experimental study. Acta Neurochir 152(4):655–661

    Article  Google Scholar 

  53. de Kelft EV, Costa F, der Planken DV, Schils F (2012) A prospective multicenter registry on the accuracy of pedicle screw placement in the thoracic, lumbar, and sacral levels with the use of the O-arm imaging system and stealthstation navigation. Spine 37(25):E1580–E1587

    Article  Google Scholar 

  54. Kilian P et al (2008) New visualization tools: computer vision and ultrasound for MIS navigation. Int J Med Robot Comput Assist Surg 4(1):23–31

    Article  MathSciNet  Google Scholar 

  55. Kim Y, Kim KI, hyeok Choi J, Lee K (2011) Novel methods for 3D postoperative analysis of total knee arthroplasty using 2D–3D image registration. Clin Biomech 26(4):384–391

    Article  Google Scholar 

  56. Kobayashi K, Sakamoto M, Tanabe Y, Ariumi A, Sato T, Omori G, Koga Y (2009) Automated image registration for assessing three-dimensional alignment of entire lower extremity and implant position using bi-plane radiography. J Biomech 42(16):2818–2822

    Article  Google Scholar 

  57. Kowal J, Amstutz C, Langlotz F, Talib H, Ballester MG (2007) Automated bone contour detection in ultrasound B-mode images for minimally invasive registration in computer-assisted surgery—an in vitro evaluation. Int J Med Robot Comput Assist Surg 3(4):341–348

    Article  Google Scholar 

  58. Kozic N, Weber S, Büchler P, Lutz C, Reimers N, Ballester MÁG, Reyes M (2010) Optimisation of orthopaedic implant design using statistical shape space analysis based on level sets. Med Image Anal 14(3):265–275

    Article  Google Scholar 

  59. van de Kraats EB, Penney GP, Tomaževič D, van Walsum T, Niessen WJ (2005) Standardized evaluation methodology for 2-D–3-D registration. IEEE Trans Med Imag 24(9):1177–1189

    Article  Google Scholar 

  60. Kunz M, Ma B, Rudan JF, Ellis RE, Pichora DR (2013) Image-guided distal radius osteotomy using patient- specific instrument guides. J Hand Surg Am 38(8):1618–24

    Google Scholar 

  61. Lang A, Mousavi P, Gill S, Fichtinger G, Abolmaesumi P (2012) Multi-modal registration of speckle-tracked freehand 3D ultrasound to CT in the lumbar spine. Med Image Anal 16(3):675–686

    Article  Google Scholar 

  62. Lehmann TM, Gönner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imag 18(11):1049–1075

    Article  Google Scholar 

  63. Letta C, Schweizer A,, Fürnstahl P (2014) Quantification of contralateral differences of the scaphoid: a comparison of bone geometry in three dimensions. Anat Res Int 2014:904275

    Google Scholar 

  64. Lin CC et al (2013) Intervertebral anticollision constraints improve out-of-plane translation accuracy of a single-plane fluoroscopy-to-CT registration method for measuring spinal motion. Med Phys 40(3):031–912

    Article  Google Scholar 

  65. Livyatan H, Yaniv Z, Joskowicz L (2002) Robust automatic C-arm calibration for fluoroscopy-based navigation: a practical approach. In: Dohi T et al (eds) Medical image computing and computer-assisted intervention, pp 60–68

    Google Scholar 

  66. Livyatan H, Yaniv Z, Joskowicz L (2003) Gradient-based 2D/3D rigid registration of fluoroscopic X-ray to CT. IEEE Trans Med Imag 22(11):1395–1406

    Article  Google Scholar 

  67. Lonner JH, John TK, Conditt MA (2010) Robotic arm-assisted UKA improves tibial component alignment a pilot study. Clin Orthop Relat Res 468(1):141–146

    Article  Google Scholar 

  68. Lu S et al (2009) A novel computer-assisted drill guide template for lumbar pedicle screw placement: a cadaveric and clinical study. Int J Med Robot Comput Assist Surg 5(2):184–191

    Article  Google Scholar 

  69. Lu S et al (2009) A novel patient-specific navigational template for cervical pedicle screw placement. Spine 34(26):E959–E964

    Article  Google Scholar 

  70. Ma B, Kunz M, Gammon B, Ellis RE, Pichora DR (2014) A laboratory comparison of computer navigation and individualized guides for distal radius osteotomy. Int J Comput Assist Radiol Surg 9(4):713–724

    Article  Google Scholar 

  71. Mahfouz MR, Hoff WA, Komistek RD, Dennis DA (2003) A robust method for registration of three-dimensional knee implant models to two-dimensional fluoroscopy images. IEEE Trans Med Imag 22(12):1561–1574

    Article  Google Scholar 

  72. Maier-Hein L, Franz AM, dos Santos TR, Schmidt M, Fangerau M, Meinzer H, Fitzpatrick JM (2012) Convergent iterative closest-point algorithm to accomodate anisotropic and inhomogenous localization error. IEEE Trans Pattern Anal Machine Intell 34(8):1520–1532

    Article  Google Scholar 

  73. Mantwill F, Schulz AP, Faber A, Hollstein D, Kammal M, Fay A, Jürgens C (2005) Robotic systems in total hip arthroplasty—is the time ripe for a new approach? Int J Med Robot Comput Assist Surg 1(4):8–19

    Article  Google Scholar 

  74. Markelj P, Tomaževič D, Pernuš F, Likar B (2008) Robust gradient-based 3-D/2-D registration of CT and MR to x-ray images. IEEE Trans Med Imag 27(12):1704–1714

    Article  Google Scholar 

  75. Markelj P, Tomaževič D, Likar B, Pernuš F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16(3):642–661

    Article  Google Scholar 

  76. Mercier L, Langø T, Lindseth F, Collins DL (2005) A review of calibration techniques for freehand 3-D ultrasound systems. Ultrasound Med Biol 31(4):449–471

    Article  Google Scholar 

  77. Moghari MH, Abolmaesumi P (2007) Point-based rigid-body registration using an unscented Kalman filter. IEEE Trans Med Imag 26(12):1708–1728

    Article  Google Scholar 

  78. Momi ED, Cerveri P, Gambaretto E, Marchente M, Effretti O, Barbariga S, Gini G, Ferrigno G (2008) Robotic alignment of femoral cutting mask during total knee arthroplasty. Int J Comput Assist Radiol Surg 3(5):413–419

    Article  Google Scholar 

  79. Mozes A, Chang TC, Arata L, Zhao W (2010) Three-dimensional A-mode ultrasound calibration and registration for robotic orthopaedic knee surgery. Int J Med Robot Comput Assist Surg 6(1):91–101

    Google Scholar 

  80. Najafi M, Afsham N, Abolmaesumi P, Rohling R (2014) A closed-form differential formulation for ultrasound spatial calibration: multi-wedge phantom. Ultrasound Med Biol 40(9):2231–2243

    Article  Google Scholar 

  81. Nakamura N, Sugano N, Nishii T, Miki H, Kakimoto A, Yamamura M (2009) Robot-assisted primary cementless total hip arthroplasty using surface registration techniques: a short-term clinical report. Int J Comput Assist Radiol Surg 4(2):157–162

    Article  Google Scholar 

  82. Nogler M, Maurer H, Wimmer C, Gegenhuber C, Bach C, Krismer M (2001) Knee pain caused by a fiducial marker in the medial femoral condyle. Acta Orthop Scand 72(5):477–480

    Article  Google Scholar 

  83. Oertel MF, Hobart J, Stein M, Schreiber V, Scharbrodt W (2011) Clinical and methodological precision of spinal navigation assisted by 3D intraoperative O-arm radiographic imaging. J Neurosurg Spine 14(4):532–536

    Article  Google Scholar 

  84. Ohta N, Kanatani K (1998) Optimal estimation of three-dimensional rotation and reliability evaluation. In: Computer vision—ECCV’98, LNCS, vol 1406. pp 175–187

    Google Scholar 

  85. Oka K, Moritomo H, Goto A, Sugamoto K, Yoshikawa H, Murase T (2008) Corrective osteotomy for malunited intra-articular fracture of the distal radius using a custom-made surgical guide based on three-dimensional computer simulation: case report. J Hand Surg Am 33(6):835–840

    Article  Google Scholar 

  86. Okada T, Iwasaki Y, Koyama T, Sugano N, Chen Y, Yonenobu K, Sato Y (2009) Computer-assisted preoperative planning for reduction of proximal femoral fracture using 3-D-CT data. IEEE Trans Biomed Eng 56(3):749–759

    Article  Google Scholar 

  87. Otake Y, Armand M, Armiger RS, Kutzer MDM, Basafa E, Kazanzides P, Taylor RH (2012) Intraoperative image-based multiview 2D/3D registration for image-guided orthopaedic surgery: incorporation of fiducial-based C-arm tracking and GPU-acceleration. IEEE Trans Med Imag 31(4):948–962

    Article  Google Scholar 

  88. Otake Y, Schafer S, Stayman JW, Zbijewski W, Kleinszig G, Graumann R, Khanna AJ, Siewerdsen JH (2012) Automatic localization of vertebral levels in X-ray fluoroscopy using 3D–2D registration: a tool to reduce wrong-site surgery. Phys Med Biol 57(17):5485–5508

    Google Scholar 

  89. Otake Y, Wang AS, Stayman JW, Uneri A, Kleinszig G, Vogt S, Khanna AJ, Gokaslan ZL, Siewerdsen JH (2013) Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation. Phys Med Biol 58(23):8535–8553

    Article  Google Scholar 

  90. Otomaru I, Nakamoto M, Kagiyama Y, Takao M, Sugano N, Tomiyama N, Tada Y, Sato Y (2012) Automated preoperative planning of femoral stem in total hip arthroplasty from 3D CT data: Atlas-based approach and comparative study. Med Image Anal 16(2):415–426

    Article  Google Scholar 

  91. Pawiro SA, Markelj P, Pernus F, Gendrin C, Figl M, Weber C, Kainberger F, Nobauer-Huhmann I, Bergmeister H, Stock M, Georg D, Bergmann H, Birkfellner W (2011) Validation for 2D/3D registration I: a new gold standard data set. Med Phys 38(3):1481–1490

    Article  Google Scholar 

  92. Penney GP, Weese J, Little JA, Desmedt P, Hill DLG, Hawkes DJ (1998) A comparison of similarity measures for use in 2D-3D medical image registration. IEEE Trans Med Imag 17(4):586–595

    Article  Google Scholar 

  93. Penney GP, Batchelor PG, Hill DLG, Hawkes DJ, Weese J (2001) Validation of a two-to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images. Med Phys 28(6):1024–1032

    Article  Google Scholar 

  94. Penney GP, Barratt DC, Chan CSK, Slomczykowski M, Carter TJ, Edwards PJ, Hawkes DJ (2006) Cadaver validation of intensity-based ultrasound to CT registration. Med Image Anal 10(3):385–395

    Article  Google Scholar 

  95. Penney GP, Edwards PJ, Hipwell JH, Slomczykowski M, Revie I, Hawkes DJ (2007) Postoperative calculation of acetabular cup position using 2-D-3-D registration. IEEE Trans Biomed Eng 54(7):1342–1348

    Article  Google Scholar 

  96. Petermann J, Kober R, Heinze R, Frölich JJ, Heeckt PF, Gotzen L (2000) Computer-assisted planning and robot-assisted surgery in anterior cruciate ligament reconstruction. Operative Tech Orthop 10(1):50–55

    Article  Google Scholar 

  97. Quiñones-Hinojosa A, Kolen ER, Jun P, Rosenberg WS, Weinstein PR (2006) Accuracy over space and time of computer-assisted fluoroscopic navigation in the lumbar spine in vivo. J Spinal Disord Tech 19(2):109–113

    Google Scholar 

  98. Radermacher K, Portheine F, Anton M, Zimolong A, Kaspers G, Rau G, Staudte HW (1998) Computer assisted orthopaedic surgery with image based individual templates. Clin Orthop Relat Res Sep 354:28–38

    Article  Google Scholar 

  99. Rajamani KT, Styner MA, Talib H, Zheng G, Nolte L, Ballester MÁG (2007) Statistical deformable bone models for robust 3D surface extrapolation from sparse data. Med Image Anal 11(2):99–109

    Article  Google Scholar 

  100. Rasoulian A, Abolmaesumi P, Mousavi P (2012) Feature-based multibody rigid registration of CT and ultrasound images of lumbar spine. Med Phys 39(6):3154–3166

    Article  Google Scholar 

  101. Richter M, Zech S (2008) 3D imaging (ARCADIS)-based computer assisted surgery (CAS) guided retrograde drilling in osteochondritis dissecans of the talus. Foot Ankle Int 29(12):1243–1248

    Article  Google Scholar 

  102. Rieger M, Gabl M, Gruber H, Jaschke WR, Mallouhi A (2005) CT virtual reality in the preoperative workup of malunited distal radius fractures: preliminary results. Eur Radiol 4(15):792–797

    Article  Google Scholar 

  103. Rodriguez F et al (2005) Robotic clinical trials of uni-condylar arthroplasty. Int J Med Robot Comput Assist Surg 1(4):20–28

    Article  Google Scholar 

  104. Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Non-rigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imag 18(8):712–721

    Article  Google Scholar 

  105. Rusinkiewicz S, Levoy M (2001) Efficient variants of the ICP algorithm. In: International conference on 3D digital imaging and modeling, pp 145–152

    Google Scholar 

  106. Russakoff DB, Rohlfing T, Adler JR Jr, Maurer CR Jr (2005) Intensity-based 2D–3D spine image registration incorporating a single fiducial marker. Acad Radiol 12(1):37–50

    Article  Google Scholar 

  107. Schafer S et al (2011) Mobile C-arm cone-beam CT for guidance of spine surgery: Image quality, radiation dose, and integration with interventional guidance. Med Phys 38(8):4563–4574

    Article  Google Scholar 

  108. Schuler B, Fritscher KD, Kuhn V, Eckstein F, Link TM, Schubert R (2010) Assessment of the individual fracture risk of the proximal femur by using statistical appearance models. Med Phys 37(6):2560–2571

    Article  Google Scholar 

  109. Schulz AP, Seide K, Queitsch C, von Haugwitz A, Meiners J, Kienast B, Tarabolsi M, Kammal M, Jürgens C (2007) Results of total hip replacement using the Robodoc surgical assistant system: clinical outcome and evaluation of complications for 97 procedures. Int J Med Robot Comput Assist Surg 3(4):301–306

    Article  Google Scholar 

  110. Schumann S, Nolte LP, Zheng G (2012) Determination of pelvic orientation from sparse ultrasound data for THA operated in the lateral position. Int J Med Robot Comput Assist Surg 8(1):107–113

    Article  Google Scholar 

  111. Schweizer A, Fürnstahl P, Harders M, Székely G, Nagy L (2010) Complex radius shaft malunion: osteotomy with computer-assisted planning. HAND 5:171–178

    Article  Google Scholar 

  112. Shamir RR, Joskowicz L, Spektor S, Shoshan Y (2009) Localization and registration accuracy in image guided neurosurgery: a clinical study. Int J Comput Assist Radiol Surg 4(1):45–52

    Article  Google Scholar 

  113. Shoham M, Burman M, Zehavi E, Joskowicz L, Batkilin E, Kunicher Y (2003) Bone-mounted miniature robot for surgical procedures: concept and clinical applications. IEEE Trans Robot Automat 19(5):893–901

    Article  Google Scholar 

  114. Shoham M et al (2007) Robotic assisted spinal surgery—from concept to clinical practice. Comput Aided Surg 12(2):105–115

    MathSciNet  Google Scholar 

  115. Smith EJ, Al-Sanawi H, Gammon B, John PS, Pichora DR, Ellis RE (2012) Volume slicing of cone-beam computed tomography images for navigation of percutaneous scaphoid fixation. Int J Comput Assist Radiol Surg 7(3):433–444

    Article  Google Scholar 

  116. Smith JR, Riches PE, Rowe PJ (2014) Accuracy of a freehand sculpting tool for unicondylar knee replacement. Int J Med Robot Comput Assist Surg 10(2):162–169

    Article  Google Scholar 

  117. Stindel E, Briard JL, Merloz P, Plaweski S, Dubrana F, Lefevre C, Troccaz J (2002) Bone morphing: 3D morphological data for total knee arthroplasty. Comput Aided Surg 7(3):156–168

    Article  Google Scholar 

  118. Stöckle U, Schaser K, König B (2007) Image guidance in pelvic and acetabular surgery-expectations, success and limitations. Injury 38(4):450–462

    Article  Google Scholar 

  119. Sugano N (2013) Computer-assisted orthopaedic surgery and robotic surgery in total hip arthroplasty. Clin Orthop Surg 5(1):1–9

    Article  Google Scholar 

  120. Talib H, Peterhans M, Garća J, Styner M, Ballester MAG (2011) Information filtering for ultrasound-based real-time registration. IEEE Trans Biomed Eng 58(3):531–540

    Article  Google Scholar 

  121. Tate PM, Lachine V, Fu L, Croitoru H, Sati M (2001) Performance and robustness of automatic fluoroscopic image calibration in a new computer assisted surgery system. In: Medical image computing and computer-assisted intervention, pp 1130–1136

    Google Scholar 

  122. Tensho K, Kodaira H, Yasuda G, Yoshimura Y, Narita N, Morioka S, Kato H, Saito N (2011) Anatomic double-bundle anterior cruciate ligament reconstruction, using CT-based navigation and fiducial markers. Knee Surg Sports Traumatol Arthrosc 19(3):378–383

    Google Scholar 

  123. Tomaževič D, Likar B, Pernuš F (2004) “Gold standard” data for evaluation and comparison of 3D/2D registration methods. Comput Aided Surg 9(4):137–144

    Article  Google Scholar 

  124. Tomaževič D, Likar B, Slivnik T, Pernuš F (2003) 3-D/2-D registration of CT and MR to X-ray images. IEEE Trans Med Imag 22(11):1407–1416

    Article  Google Scholar 

  125. Tomaževič D, Likar B, Pernuš F (2006) 3-D/2-D registration by integrating 2-D information in 3-D. IEEE Trans Med Imag 25(1):17–27

    Article  Google Scholar 

  126. Tornai GJ, Pappasa GC (2012) Fast DRR generation for 2D to 3D registration on GPUs. Med Phys 39(8):4795–4799

    Article  Google Scholar 

  127. Tsai TY, Lu TW, Chen CM, Kuo MY, Hsu HC (2010) A volumetric model-based 2D to 3D registration method for measuring kinematics of natural knees with single-plane fluoroscopy. Med Phys 37(3):1273–1284

    Article  Google Scholar 

  128. Umeyama S (1991) Least-squares estimation of transformation parameters between two point patterns. IEEE Trans Pattern Anal Mach Intell 13(4):376–380

    Article  Google Scholar 

  129. Uneri A, Otake Y, Wang AS, Kleinszig G, Vogt S, Khanna AJ, Siewerdsen JH (2014) 3D–2D registration for surgical guidance: effect of projection view angles on registration accuracy. Phys Med Biol 59(2):271–287

    Article  Google Scholar 

  130. Varnavas A, Carrell T, Penney GP (2013) Increasing the automation of a 2D–3D registration system. IEEE Trans Med Imag 32(2):387–399

    Article  Google Scholar 

  131. Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1, Supplement 1):S61–S72

    Google Scholar 

  132. Walker MW, Shao L, Volz RA (1991) Estimating 3-D location parameters using dual number quaternions. CVGIP Image Underst 54(3):358–367

    Article  MATH  Google Scholar 

  133. Wei W, Schön N, Dannenmann T, Petzold R (2011) Determining the position of a patient reference from C-Arm views for image guided navigation. Int J Comput Assist Radiol Surg 6(2):217–227

    Article  Google Scholar 

  134. Weil YA, Liebergall M, Mosheiff R, Singer SB, Joskowicz L, Khoury A (2011) Assessment of two 3-D fluoroscopic systems for articular fracture reduction: a cadaver study. Int J Comput Assist Radiol Surg 6(5):685–692

    Google Scholar 

  135. Wein W, Brunke S, Khamene A, Callstrom MR, Navab N (2008) Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal 12:577–585

    Article  Google Scholar 

  136. West J et al (1997) Comparison and evaluation of retrospective intermodality brain image registration techniques. J Comput Assist Tomogr 4(4):554–568

    Article  Google Scholar 

  137. Whitmarsh T, Humbert L, Craene MD, Barquero LMDR, Frangi AF (2011) Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy X-ray absorptiometry. IEEE Trans Med Imag 12(30):2101–2114

    Article  Google Scholar 

  138. Wiles AD, Likholyot A, Frantz DD, Peters TM (2008) A statistical model for point-based target registration error with anisotropic fiducial localizer error. IEEE Trans Med Imag 27(3):378–390

    Article  Google Scholar 

  139. Wing JM (2006) Computational thinking. Commun ACM 49(3):33–35

    Article  MathSciNet  Google Scholar 

  140. Xie W, Franke J, Chen C, Grützner PA, Schumann S, Nolte L, Zheng G (2014) Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs. Int J Comput Assist Radiol Surg 9(2):165–176

    Article  Google Scholar 

  141. Yan CXB, Goulet B, Pelletier J, Chen SJS, Tampieri D, Collins DL (2011) Towards accurate, robust and practical ultrasound-CT registration of vertebrae for image-guided spine surgery. Int J Comput Assist Radiol Surg 6(4):523–537

    Article  Google Scholar 

  142. Yan CXB, Goulet B, Chen SJ, Tampieri D, Collins DL (2012) Validation of automated ultrasound-ct registration of vertebrae. Int J Comput Assist Radiol Surg 7(4):601–610

    Article  Google Scholar 

  143. Yan CXB, Goulet B, Tampieri D, Collins DL (2012) Ultrasound-CT registration of vertebrae without reconstruction. Int J Comput Assist Radiol Surg 7(6):901–909

    Article  Google Scholar 

  144. Yaniv Z (2009) Localizing spherical fiducials in C-arm based cone-beam CT. Med Phys 36(11):4957–4966

    Article  Google Scholar 

  145. Yaniv Z (2010) Evaluation of spherical fiducial localization in C-arm cone-beam CT using patient data. Med Phys 37(10):5298–5305

    Article  Google Scholar 

  146. Yaniv Z, Joskowicz L (2005) Precise robot-assisted guide positioning for distal locking of intramedullary nails. IEEE Trans Med Imag 24(5):624–635

    Article  Google Scholar 

  147. Zeng X, Wang C, Zhou H, Wei S, Chen X (2014) Low-dose three-dimensional reconstruction of the femur with unit free-form deformation. Med Phys 41(8):081–911

    Article  Google Scholar 

  148. Zhang YZ, Chen B, Lu S, Yang Y, Zhao JM, Liu R, Li YB, Pei GX (2011) Preliminary application of computer-assisted patient-specific acetabular navigational template for total hip arthroplasty in adult single development dysplasia of the hip. Int J Med Robot Comput Assist Surg 7(4):469–474

    Article  Google Scholar 

  149. Zhang Z (1994) Iterative point matching for registration of free-form curves and surfaces. Int J Comput Vision 13(2):119–152

    Article  Google Scholar 

  150. Zheng G, Zhang X (2009) Robust automatic detection and removal of fiducial projections in fluoroscopy images: an integrated solution. Med Eng Phys 31(5):571–580

    Article  Google Scholar 

  151. Zheng G, Zhang X (2010) Computer assisted determination of acetabular cup orientation using 2D–3D image registration. Int J Comput Assist Radiol Surg 5(5):437–447

    Article  Google Scholar 

  152. Zheng G, Gollmer S, Schumann S, Dong X, Feilkas T, Ballester MÁG (2009) A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images. Med Image Anal 13(6):883–899

    Article  Google Scholar 

  153. Zheng G, Zhang X, Steppacher SD, Murphy SB, Siebenrock K, Tannast M (2009) Hipmatch: an object-oriented cross-platform program for accurate determination of cup orientation using 2D–3D registration of single standard X-ray radiograph and a CT volume. Comput Methods Programs Biomed 95(3):236–248

    Article  Google Scholar 

  154. Zheng G, Schumann S, Ballester MÁG (2010) An integrated approach for reconstructing a surface model of the proximal femur from sparse input data and a multi-resolution point distribution model: an in vitro study. Int J Comput Assist Radiol Surg 5(1):99–107

    Article  Google Scholar 

  155. Zheng G, von Recum J, Nolte L, Grützner PA, Steppacher SD, Franke J (2012) Validation of a statistical shape model-based 2D/3D reconstruction method for determination of cup orientation after THA. Int J Comput Assist Radiol Surg 7(2):225–231

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TAJ Technologies Inc., Mendota Heights, MN, 55120, USA

    Ziv Yaniv

  2. Office of High Performance Computing and Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA

    Ziv Yaniv

Authors
  1. Ziv Yaniv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ziv Yaniv .

Editor information

Editors and Affiliations

  1. Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland

    Guoyan Zheng

  2. GE Healthcare and University of Western Ontario, London, Ontario, Canada

    Shuo Li

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Yaniv, Z. (2016). Registration for Orthopaedic Interventions. In: Zheng, G., Li, S. (eds) Computational Radiology for Orthopaedic Interventions. Lecture Notes in Computational Vision and Biomechanics, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-23482-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23482-3_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23481-6

  • Online ISBN: 978-3-319-23482-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation