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Image Fusion Principles: Theory

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Interventional Urology

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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.

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References

  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.

    Article  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  4. Peters TM. Image-guidance for surgical procedures. Phys Med Biol. 2006;51:R505–40.

    Article  PubMed  Google Scholar 

  5. Webb S the physics of medical imaging institute of physics publishing 1988; 8.

    Google Scholar 

  6. Hayne R, Meyers JR. Characteristics of electrical activity of human corpus striatum and neighboring structures. J Neurophysiol. 1949;12:185–95.

    CAS  PubMed  Google Scholar 

  7. al-Rodhan NR, Kelly PJ. Pioneers of stereotactic neurosurgery. Stereotact Funct Neurosurg. 1992;58:60–6.

    Article  CAS  PubMed  Google Scholar 

  8. Galloway RL. The process and development of image-guided procedures. Annu Rev Biomed Eng. 2001;3:83–108.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  10. Pearlman CK. Transrectal biopsy of the prostate. J Urol. 1955;74:387–92.

    CAS  PubMed  Google Scholar 

  11. Needell MH, Slotkin GE, Mitchell FD, et al. Prostatic needle biopsy. J Urol. 1955;74:138–41.

    CAS  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  13. George AK, Pinto PA, Rais-Bahrami S. Multiparametric MRI in the PSA screening era. Biomed Res Int. 2014;2014:465816.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  19. Mahesh M. Fluoroscopy: patient radiation exposure issues. Radiographics. 2001;21:1033–45.

    Article  CAS  PubMed  Google Scholar 

  20. Gouraud H. Continuous shading of curved surfaces. IEEE Trans Comput. 1971;C-20:87–93.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  23. Udupa JK. Three-dimensional visualization and analysis methodologies: a current perspective. Radiographics. 1999;19:783–806.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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. Hutton BF, Braun M. Software for image registration: algorithms, accuracy, efficacy. Semin Nucl Med. 2003;33:180–92.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  30. Oliveira FP, Tavares JM. Medical image registration: a review. Comput Methods Biomech Biomed Engin. 2014;17:73–93.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  37. Chen GTY, Pelizzari CA. Image correlation techniques in radiation therapy treatment planning. Comput Med Imaging Graph. 1988;13:235–40.

    Article  Google Scholar 

  38. Besl PJ, McKay NDA. Method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell. 1992;14:239–56.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. 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.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    PubMed Central  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  45. Hassfeld S, Muhling J, Zoller J. Intraoperative navigation in oral and maxillofacial surgery. Int J Oral Maxillofac Surg. 1995;24:111–9.

    Article  CAS  PubMed  Google Scholar 

  46. Phee SJ, Yang K. Interventional navigation systems for treatment of unresectable liver tumor. Med Biol Eng Comput. 2010;48:103–11.

    Article  PubMed  Google Scholar 

  47. Wood BJ, Kruecker J, Abi-Jaoudeh N, et al. Navigation systems for ablation. J Vasc Interv Radiol. 2010;21:S257–63.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  49. Adams L, Krybus W, Meyer-Ebrecht D, et al. Computer assisted surgery. IEEE Compu Graph. 1990;10:43–51.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  55. Yaniv Z, Wilson E, Lindisch D, et al. Electromagnetic tracking in the clinical environment. Med Phys. 2009;36:876–92.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  60. Nicolau S, Soler L, Mutter D, et al. Augmented reality in laparoscopic surgical oncology. Surg Oncol. 2011;20:189–201.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  63. Simpfendorfer T, Baumhauer M, Muller M, et al. Augmented reality visualization during laparoscopic radical prostatectomy. J Endourol. 2011;25:1841–5.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  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.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Arvin K. George MD & John Michael DiBianco MD

  2. Departments of Urology and Radiology, Associate Professor of Urology and Radiology and the Director of Focal Therapy and Interventional Urologic Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ardeshir R. Rastinehad DO

Authors
  1. Arvin K. George MD
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  2. John Michael DiBianco MD
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  3. Ardeshir R. Rastinehad DO
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Corresponding author

Correspondence to Arvin K. George MD .

Editor information

Editors and Affiliations

  1. Focal Therapy and Interventional Urologic Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Ardeshir R. Rastinehad

  2. Department of Radiology, Hofstra North Shore LIJ School of Medici Department of Radiology, New Hyde Park, New York, USA

    David N. Siegel

  3. National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA

    Peter A. Pinto

  4. National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA

    Bradford J. Wood

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George, A.K., DiBianco, J.M., Rastinehad, A.R. (2016). Image Fusion Principles: Theory. In: Rastinehad, A., Siegel, D., Pinto, P., Wood, B. (eds) Interventional Urology. Springer, Cham. https://doi.org/10.1007/978-3-319-23464-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-23464-9_3

  • Publisher Name: Springer, Cham

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