Image Fusion Principles: Theory

  • Arvin K. George
  • John Michael DiBianco
  • Ardeshir R. Rastinehad

Abstract

Imaging is an integral component in the investigation of urologic disease. Continued advancement in both hardware and software tools have given rise to novel image modalities and innovative approaches for diagnosis and intervention. An appreciation of the fundamentals of imaging techniques is essential to enable Urologists to continually employ them in routine clinical practice. The objective of this chapter is to review the current state of the art regarding techniques in imaging and their applications in urologic interventions, with special attention to registration, image fusion, and tracking in diagnostic and therapeutic implementation.

Keywords

Registration Image-guided surgery Image fusion Optical tracking EM tracking 

References

  1. 1.
    Lemaitre G, Marti R, Freixenet J, et al. Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput Biol Med. 2015;60C:8–31.CrossRefGoogle Scholar
  2. 2.
    Smeenge M, Mischi M, Laguna Pes MP, et al. Novel contrast-enhanced ultrasound imaging in prostate cancer. World J Urol. 2011;29:581–7.PubMedCentralCrossRefPubMedGoogle Scholar
  3. 3.
    Loch T, Carey B, Walz J, et al. EAU standardised medical terminology for urologic imaging: a taxonomic approach. Eur Urol. 2014;67(5):965–71.CrossRefPubMedGoogle Scholar
  4. 4.
    Peters TM. Image-guidance for surgical procedures. Phys Med Biol. 2006;51:R505–40.CrossRefPubMedGoogle Scholar
  5. 5.
    Webb S the physics of medical imaging institute of physics publishing 1988; 8.Google Scholar
  6. 6.
    Hayne R, Meyers JR. Characteristics of electrical activity of human corpus striatum and neighboring structures. J Neurophysiol. 1949;12:185–95.PubMedGoogle Scholar
  7. 7.
    al-Rodhan NR, Kelly PJ. Pioneers of stereotactic neurosurgery. Stereotact Funct Neurosurg. 1992;58:60–6.CrossRefPubMedGoogle Scholar
  8. 8.
    Galloway RL. The process and development of image-guided procedures. Annu Rev Biomed Eng. 2001;3:83–108.CrossRefPubMedGoogle Scholar
  9. 9.
    Lee F, Torp-Pedersen ST, Siders DB. The role of transrectal ultrasound in the early detection of prostate cancer. CA Cancer J Clin. 1989;39:337–60.CrossRefPubMedGoogle Scholar
  10. 10.
    Pearlman CK. Transrectal biopsy of the prostate. J Urol. 1955;74:387–92.PubMedGoogle Scholar
  11. 11.
    Needell MH, Slotkin GE, Mitchell FD, et al. Prostatic needle biopsy. J Urol. 1955;74:138–41.PubMedGoogle Scholar
  12. 12.
    Onik G, Miessau M, Bostwick DG. Three-dimensional prostate mapping biopsy has a potentially significant impact on prostate cancer management. J Clini Oncol. 2009;27:4321–6.CrossRefGoogle Scholar
  13. 13.
    George AK, Pinto PA, Rais-Bahrami S. Multiparametric MRI in the PSA screening era. Biomed Res Int. 2014;2014:465816.PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Rastinehad AR, Baccala Jr AA, Chung PH, et al. D’Amico risk stratification correlates with degree of suspicion of prostate cancer on multiparametric magnetic resonance imaging. J Urol. 2011;185:815–20.PubMedCentralCrossRefPubMedGoogle Scholar
  15. 15.
    Xu S, Kruecker J, Turkbey B, et al. Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies. Comput Aided Surg. 2008;13:255–64.PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Neuzillet Y, Lechevallier E, Andre M, et al. Accuracy and clinical role of fine needle percutaneous biopsy with computerized tomography guidance of small (less than 4.0 cm) renal masses. J Urol. 2004;171:1802–5.CrossRefPubMedGoogle Scholar
  17. 17.
    Hedlund E, Karlsson JE, Starck SA. Automatic and manual image fusion of in-pentetreotide SPECT and diagnostic CT in neuroendocrine tumor imaging - an evaluation. J Med Phy. 2010;35:223–8.CrossRefGoogle Scholar
  18. 18.
    Smith-Bindman R, Aubin C, Bailitz J, et al. Ultrasonography versus computed tomography for suspected nephrolithiasis. N Engl J Med. 2014;371:1100–10.CrossRefPubMedGoogle Scholar
  19. 19.
    Mahesh M. Fluoroscopy: patient radiation exposure issues. Radiographics. 2001;21:1033–45.CrossRefPubMedGoogle Scholar
  20. 20.
    Gouraud H. Continuous shading of curved surfaces. IEEE Trans Comput. 1971;C-20:87–93.CrossRefGoogle Scholar
  21. 21.
    Udupa JK, Hung HM, Chuang KS. Surface and volume rendering in three-dimensional imaging: a comparison. J Digit Imaging. 1991;1: 4:159–68.CrossRefGoogle Scholar
  22. 22.
    Schreiner S, Galloway RL, Paschal CB. Comparison of projection algorithms used for the construction of maximum intensity projection images. J Comput Assist Tomogr. 1996;20:56–67.CrossRefPubMedGoogle Scholar
  23. 23.
    Udupa JK. Three-dimensional visualization and analysis methodologies: a current perspective. Radiographics. 1999;19:783–806.CrossRefPubMedGoogle Scholar
  24. 24.
    Miller K, Wittek A, Joldes G, et al. Modelling brain deformations for computer-integrated neurosurgery. Int J Numer Meth Biomed Eng. 2010;26:117–38.CrossRefGoogle Scholar
  25. 25.
    Foskey M, Davis B, Goyal L, et al. Large deformation three-dimensional image registration in image-guided radiation therapy. Phys Med Biol. 2005;50:5869–92.CrossRefPubMedGoogle Scholar
  26. 26.
    King AP, Rhode KS, Ma Y, et al. Registering preprocedure volumetric images with intraprocedure 3-D ultrasound using an ultrasound imaging model. IEEE Trans Med Imaging. 2010;29:924–37.CrossRefPubMedGoogle Scholar
  27. 27.
    Sankineni S, George AK, Brown AM, et al. Posterior subcapsular prostate cancer: identification with mpMRI and MRI/TRUS fusion-guided biopsy. Abdom Imaging. 2015.Google Scholar
  28. 28.
    Hutton BF, Braun M. Software for image registration: algorithms, accuracy, efficacy. Semin Nucl Med. 2003;33:180–92.CrossRefPubMedGoogle Scholar
  29. 29.
    Hill DL, Hawkes DJ, Crossman JE, et al. Registration of MR and CT images for skull base surgery using point-like anatomical features. Br J Radiol. 1991;64:1030–5.CrossRefPubMedGoogle Scholar
  30. 30.
    Oliveira FP, Tavares JM. Medical image registration: a review. Comput Methods Biomech Biomed Engin. 2014;17:73–93.CrossRefPubMedGoogle Scholar
  31. 31.
    Ukimura O, Hirahara N, Fujihara A, et al. Technique for a hybrid system of real-time transrectal ultrasound with preoperative magnetic resonance imaging in the guidance of targeted prostate biopsy. Int J Urol. 2010;17:890–3.CrossRefPubMedGoogle Scholar
  32. 32.
    West J, Fitzpatrick JM, Wang MY, et al. Comparison and evaluation of retrospective intermodality brain image registration techniques. J Comput Assist Tomogr. 1997;21:554–66.CrossRefPubMedGoogle Scholar
  33. 33.
    Barnden L, Kwiatek R, Lau Y, et al. Validation of fully automatic brain SPET to MR co-registration. Eur J Nucl Med. 2000;27:147–54.CrossRefPubMedGoogle Scholar
  34. 34.
    Wong JC, Studholme C, Hawkes DJ, et al. Evaluation of the limits of visual detection of image misregistration in a brain fluorine-18 fluorodeoxyglucose PET-MRI study. Eur J Nucl Med. 1997;24:642–50.PubMedGoogle Scholar
  35. 35.
    Fitzpatrick JM, Hill DL, Shyr Y, et al. Visual assessment of the accuracy of retrospective registration of MR and CT images of the brain. IEEE Trans Med Imaging. 1998;17:571–85.CrossRefPubMedGoogle Scholar
  36. 36.
    Pelizzari CA, Chen GT, Spelbring DR, et al. Accurate three-dimensional registration of CT, PET, and/or MR images of the brain. J Comput Assist Tomogr. 1989;13:20–6.CrossRefPubMedGoogle Scholar
  37. 37.
    Chen GTY, Pelizzari CA. Image correlation techniques in radiation therapy treatment planning. Comput Med Imaging Graph. 1988;13:235–40.CrossRefGoogle Scholar
  38. 38.
    Besl PJ, McKay NDA. Method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell. 1992;14:239–56.CrossRefGoogle Scholar
  39. 39.
    Lee D, Nam WH, Lee JY, et al. Non-rigid registration between 3D ultrasound and CT images of the liver based on intensity and gradient information. Phys Med Biol. 2011;56:117–37.CrossRefPubMedGoogle Scholar
  40. 40.
    Hill DL, Studholme C, Hawkes DJ. Voxel similarity measures for automated image registration. In: Roba RA, editor. Visualization in biomedical computing. 1994. p. 205–16.Google Scholar
  41. 41.
    Krucker J, Xu S, Glossop N, et al. Electromagnetic tracking for thermal ablation and biopsy guidance: clinical evaluation of spatial accuracy. J Vasc Interv Radiol. 2007;18:1141–50.PubMedCentralCrossRefPubMedGoogle Scholar
  42. 42.
    Giesel FL, Mehndiratta A, Locklin J, et al. Image fusion using CT, MRI and PET for treatment planning, navigation and follow up in percutaneous RFA. Exp Oncol. 2009;31:106–14.PubMedCentralPubMedGoogle Scholar
  43. 43.
    Schwarz Y, Greif J, Becker HD, et al. Real-time electromagnetic navigation bronchoscopy to peripheral lung lesions using overlaid CT images: the first human study. Chest. 2006;129:988–94.CrossRefPubMedGoogle Scholar
  44. 44.
    Peng JL, Kahler D, Li JG, et al. Characterization of a real-time surface image-guided stereotactic positioning system. Med Phys. 2010;37:5421–33.CrossRefPubMedGoogle Scholar
  45. 45.
    Hassfeld S, Muhling J, Zoller J. Intraoperative navigation in oral and maxillofacial surgery. Int J Oral Maxillofac Surg. 1995;24:111–9.CrossRefPubMedGoogle Scholar
  46. 46.
    Phee SJ, Yang K. Interventional navigation systems for treatment of unresectable liver tumor. Med Biol Eng Comput. 2010;48:103–11.CrossRefPubMedGoogle Scholar
  47. 47.
    Wood BJ, Kruecker J, Abi-Jaoudeh N, et al. Navigation systems for ablation. J Vasc Interv Radiol. 2010;21:S257–63.PubMedCentralCrossRefPubMedGoogle Scholar
  48. 48.
    Schlondorff G, Mosges R, Meyer-ebrecht D, et al. [CAS (computer assisted surgery). A new procedure in head and neck surgery]. HNO. 1989;37:187–90.PubMedGoogle Scholar
  49. 49.
    Adams L, Krybus W, Meyer-Ebrecht D, et al. Computer assisted surgery. IEEE Compu Graph. 1990;10:43–51.CrossRefGoogle Scholar
  50. 50.
    Kosugi Y, Watanabe E, Goto J, et al. An articulated neurosurgical navigation system using MRI and CT images. IEEE Trans Biomed Eng. 1988;35:147–52.CrossRefPubMedGoogle Scholar
  51. 51.
    Reinhardt HF In: Taylor R, Lavallee S, Burdea G, et al., editors. Neuronavigation: a ten year review. Cambridge: MIT Press; 1995.Google Scholar
  52. 52.
    Troccaz J, Peshkin M, Davies B, et al. The use of localizers, robots and synergistic devices in CAS. Lect Notes Comput Sc. 1997;1205:725–36.CrossRefGoogle Scholar
  53. 53.
    Lugez E, Sadjadi H, Pichora DR, et al. Electromagnetic tracking in surgical and interventional environments: usability study. Int J Comput Assist Radiol Surg. 2015;10:253–62.CrossRefPubMedGoogle Scholar
  54. 54.
    Wood BJ, Zhang H, Durrani A, et al. Navigation with electromagnetic tracking for interventional radiology procedures: a feasibility study. J Vasc Interv Radiol. 2005;16:493–505.PubMedCentralCrossRefPubMedGoogle Scholar
  55. 55.
    Yaniv Z, Wilson E, Lindisch D, et al. Electromagnetic tracking in the clinical environment. Med Phys. 2009;36:876–92.PubMedCentralCrossRefPubMedGoogle Scholar
  56. 56.
    Hastenteufel M, Vetter M, Meinzer HP, et al. Effect of 3D ultrasound probes on the accuracy of electromagnetic tracking systems. Ultrasound Med Biol. 2006;32:1359–68.CrossRefPubMedGoogle Scholar
  57. 57.
    LaScalza S, Arico J, Hughes R. Effect of metal and sampling rate on accuracy of flock of birds electromagnetic tracking system. J Biomech. 2003;36:141–4.CrossRefPubMedGoogle Scholar
  58. 58.
    Hughes-Hallett A, Mayer EK, Marcus HJ, et al. Augmented reality partial nephrectomy: examining the current status and future perspectives. Urology. 2014;83:266–73.CrossRefPubMedGoogle Scholar
  59. 59.
    Greco F, Cadeddu JA, Gill IS, et al. Current perspectives in the use of molecular imaging to target surgical treatments for genitourinary cancers. Eur Urol. 2014;65:947–64.CrossRefPubMedGoogle Scholar
  60. 60.
    Nicolau S, Soler L, Mutter D, et al. Augmented reality in laparoscopic surgical oncology. Surg Oncol. 2011;20:189–201.CrossRefPubMedGoogle Scholar
  61. 61.
    Su LM, Vagvolgyi BP, Agarwal R, et al. Augmented reality during robot-assisted laparoscopic partial nephrectomy: toward real-time 3D-CT to stereoscopic video registration. Urology. 2009;73:896–900.CrossRefPubMedGoogle Scholar
  62. 62.
    Teber D, Simpfendorfer T, Guven S, et al. In-vitro evaluation of a soft-tissue navigation system for laparoscopic prostatectomy. J Endourol. 2010;24:1487–91.CrossRefPubMedGoogle Scholar
  63. 63.
    Simpfendorfer T, Baumhauer M, Muller M, et al. Augmented reality visualization during laparoscopic radical prostatectomy. J Endourol. 2011;25:1841–5.CrossRefPubMedGoogle Scholar
  64. 64.
    Nakamoto M, Ukimura O, Faber K, et al. Current progress on augmented reality visualization in endoscopic surgery. Curr Opin Urol. 2012;22:121–6.CrossRefPubMedGoogle Scholar
  65. 65.
    Rothwax JT, George AK, Wood BJ, et al. Multiparametric MRI in biopsy guidance for prostate cancer: fusion-guided. Biomed Res Int. 2014;2014:439171.PubMedCentralCrossRefPubMedGoogle Scholar
  66. 66.
    Rastinehad AR, Turkbey B, Salami SS, et al. Improving detection of clinically significant prostate cancer: magnetic resonance imaging/transrectal ultrasound fusion guided prostate biopsy. J Urol. 2014;191:1749–54.CrossRefPubMedGoogle Scholar
  67. 67.
    Wysock JS, Rosenkrantz AB, Huang WC, et al. A prospective, blinded comparison of magnetic resonance (MR) imaging-ultrasound fusion and visual estimation in the performance of MR-targeted prostate biopsy: the PROFUS trial. Eur Urol. 2014;66:343–51.CrossRefPubMedGoogle Scholar
  68. 68.
    Park BH, Jeon HG, Jeong BC, et al. Influence of magnetic resonance imaging in the decision to preserve or resect neurovascular bundles at robotic assisted laparoscopic radical prostatectomy. J Urol. 2014;192:82–8.CrossRefPubMedGoogle Scholar
  69. 69.
    Muller BG, van den Bos W, Brausi M, et al. Role of multiparametric magnetic resonance imaging (MRI) in focal therapy for prostate cancer: a Delphi consensus project. BJU Int. 2014;114:698–707.CrossRefPubMedGoogle Scholar
  70. 70.
    Partanen A, Yerram NK, Trivedi H, et al. Magnetic resonance imaging (MRI)-guided transurethral ultrasound therapy of the prostate: a preclinical study with radiological and pathological correlation using customised MRI-based moulds. BJU Int. 2013;112:508–16.CrossRefPubMedGoogle Scholar
  71. 71.
    Betrouni N, Colin P, Puech P, et al. An image guided treatment platform for prostate cancer photodynamic therapy. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:370–3.PubMedGoogle Scholar
  72. 72.
    Hegde JV, Mulkern RV, Panych LP, et al. Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J Magn Reson Imaging. 2013;37:1035–54.PubMedCentralCrossRefPubMedGoogle Scholar
  73. 73.
    Hambrock T, Vos PC, Hulsbergen-van de Kaa CA, et al. Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance. Radiology. 2013;266:521–30.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Arvin K. George
    • 1
  • John Michael DiBianco
    • 1
  • Ardeshir R. Rastinehad
    • 2
  1. 1.Urologic Oncology BranchNational Cancer Institute, National Institutes of HealthBethesdaUSA
  2. 2.Departments of Urology and Radiology, Associate Professor of Urology and Radiology and the Director of Focal Therapy and Interventional Urologic OncologyIcahn School of Medicine at Mount SinaiNew YorkUSA

Personalised recommendations