Comparison of performance between rigid and non-rigid software registering CT to FDG-PET

  • Gabriele Wolz
  • Anton Nömayr
  • Torsten Hothorn
  • Joachim Hornegger
  • Wolfgang Römer
  • Werner Bautz
  • Torsten Kuwert
Original article

Abstract

Object: This retrospective study compares the anatomical accuracy of automated rigid and non-rigid registration software for aligning data from separately performed X-ray computed tomography (CT) and positron emission tomography with F-18-deoxyglucose (PET).

Materials and methods: Analyses were performed on independently acquired PET and CT data from 40 tumor patients. Rigid as well as non-rigid automated fusion was carried out using the commercially available Mirada 7D platform (MIR and MINR, respectively) as well as a second automated non-rigid registration based on a variational image registration approach (VIR). Distances between lesion representation on PET and CT of 105 malignant lesions were measured in X-, Y-, and Z-directions. Statistical evaluation was performed using mixed effect analysis, comparing separately MIR with MINR and VIR with MINR.

Results: The percentage of lesions misregistered by less than 15 mm varied from 70% for MIR and MINR in Z-direction to 93% for VIR in X-direction. The average X-, Y- and Z-distances ranged between 5.9 ± 5.7 mm for VIR in X-direction and 12.8±9.7 mm for MIR in Z-direction. MINR was significantly more accurate than MIR in Y-direction. Furthermore, VIR aligned thoracic lesions in the X- direction significantly better than MINR.

Conclusion: The accuracy of rigid and non-rigid automated image registration can be expected to be better than 15 mm for the majority of lesions. Alignment tended to be more accurate with non-rigid registration.

Keywords

PET CT Registration Software fusion 

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References

  1. 1.
    Reinartz P, Wieres F-J, Schneider W, Schur A and Buell U (2004). Side-by-side reading of PET and CT scans in oncology: which patients might profit from integrated PET/CT. Eur J Nucl Med Mol Imaging 31: 1456–1461 PubMedCrossRefGoogle Scholar
  2. 2.
    Hutton BF, Braun M, Thurfjell L and Lau DYH (2002). Image registration: an essential tool for nuclear medicine. Eur J Nucl Med 29: 559–577 CrossRefGoogle Scholar
  3. 3.
    Lardinois D, Weder W and Hany TF et al (2003). Staging of non- small-cell lung cancer with integrated positron-emission tomography and computed tomography. N Engl J Med 348: 2500–2507 PubMedCrossRefGoogle Scholar
  4. 4.
    Townsend DW, Beyer T and Blodgett TM (2003). PET/CT scanners: a hardware approach to image fusion. Sem Nucl Med 33: 193–204 CrossRefGoogle Scholar
  5. 5.
    Beyer T, Townsend DW and Brun T et al (2000). A combined PET/CT scanner for clinical oncology. J Nucl Med 41: 1369–1379 PubMedGoogle Scholar
  6. 6.
    Hany TF, Steinert HC, Goerres GW, Buck A and von Schulthess GK (2002). PET diagnostic accuracy: improvement with in-line PET-CT system: initial results. Radiology 225: 575–581 PubMedCrossRefGoogle Scholar
  7. 7.
    Kim JH, Czernin J and Allen-Auerbach MS et al (2005). Comparison between 18F-FDG PET, in-line PET/CT, and software fusion for restaging of recurrent colorectal cancer. J Nucl Med 46: 587–595 PubMedGoogle Scholar
  8. 8.
    Allen-Auerbach M, Quon A and Weber WA et al (2004). Comparison between 2-deoxy-2–18ffluoro-D-glucose positron emission tomography/computed tomography hardware fusion for staging of patients with lymphoma. Mol Imaging Biol 6: 411–416 PubMedCrossRefGoogle Scholar
  9. 9.
    Schaffler GJ, Groell R and Schoellnast H et al (2000). Digital image fusion of CT and PET data sets-clinical value in abdominal/pelvic malignancies. J Comput Assist Tomogr 24: 644–647 PubMedCrossRefGoogle Scholar
  10. 10.
    Tsai CC, Tsai CS and Ng KK et al (2003). The impact of image fusion in resolving discrepant findings between FDG-PET and MRI/CT in patients with gynaecological cancers. Eur J Nucl Med Mol Imaging 30: 1674–1683 PubMedCrossRefGoogle Scholar
  11. 11.
    Lemke AJ, Niehues SM and Hosten N et al (2004). Retrospective digital fusion of multidetector CT and 18F-FDG PET: clinical value in pancreatic lesions–a prospective study with 104 patients. J Nucl Med 45: 1279–1286 PubMedGoogle Scholar
  12. 12.
    D’amico TA, Wong TZ, Harpole DH, Brown SD and Coleman RE (2002). Impact of computed tomography-positron emission tomography fusion in staging patients with thoracic malignancies. Ann Thorac Surg 74: 160–163 PubMedCrossRefGoogle Scholar
  13. 13.
    Rizzo G, Castiglioni I and Arienti R et al (2005). Automatic registration of PET and CT studies for clinical use in thoracic and abdominal conformal radiotherapy. QJ Nucl Med Mol Imaging 49: 267–279 Google Scholar
  14. 14.
    Halpern BS, Schiepers C and Weber WA et al (2005). Presurgical staging of non-small cell lung cancer: positron emission tomography, integrated positron emission tomography/CT, and software image fusion. Chest 128: 2289–2297 PubMedCrossRefGoogle Scholar
  15. 15.
    Römer W, Nömayr A and Greess H et al (2006). Retrospective interactive rigid fusion of F-18 FDG-PET and CT: additional diagnostic information in melanoma patients. Nuklearmedizin 45: 88–95 PubMedGoogle Scholar
  16. 16.
    Lavely WC, Scarfone C and Cevikalp H et al (2004). Phantom validation of coregistration of PET and CT for image-guided radiotherapy. Med Phys 31: 1083–1092 PubMedCrossRefGoogle Scholar
  17. 17.
    Kraus GE, Bernstein TW, Satter M, Ezzeddine B, Hwang DR and Mantil J (1995). A technique utilizing positron emission tomography and magnetic resonance/computed tomography image fusion to aid in surgical navigation and tumor volume determination. J Image Guid Surg 1: 300–307 PubMedCrossRefGoogle Scholar
  18. 18.
    Sureshbabu W and Mawlawi O (2005). PET/CT imaging artifacts. J Nucl Med Technol 33: 156–161 PubMedGoogle Scholar
  19. 19.
    Cohade C, Osman M, Marshall LN and Wahl RN (2003). PET-CT: accuracy of PET and CT spatial registration of lung lesions. Eur J Nucl Med Mol Imaging 30: 721–726 PubMedCrossRefGoogle Scholar
  20. 20.
    Goerres GW, Kamel E, Heidelberg TNH, Schwitter MR, Burger C and von Schulthess GK (2002). PET-CT image co-registration in the thorax: influence of respiration. Eur J Nucl Med Mol Imaging 29: 351–360 PubMedCrossRefGoogle Scholar
  21. 21.
    Nömayr A, Römer W and Hothorn T et al (2005). Anatomical accuracy of lesion localization: retrospective interactive rigid image registration between 18F-FDG-PET and X-ray CT. Nuklearmedizin 44: 49–55 Google Scholar
  22. 22.
    Herzog H, Tellmann L, Hocke C, Pietrzyk U, Casey ME and Kuwert T (2004). NEMA NU2–2001 guided performance evaluation of four Siemens ECAT PET-Scanners. IEEE Trans Nucl Sci 51: 2662–2669 CrossRefGoogle Scholar
  23. 23.
    Hermosillo G, Chefd’Hotel C and Faugeras O (2002). Variational methods for multi-modal image matching. Int J Comput Vision 50: 329–343 CrossRefGoogle Scholar
  24. 24.
    Hahn DA, Hornegger J, Bautz W, Kuwert T and Römer W (2005). Unbiased rigid registration using transfer functions. Proc SPIE 5747: 151–162 CrossRefGoogle Scholar
  25. 25.
    Wolz G, Nömayr A and Hothorn T et al (2007). Anatomical accuracy of interactive and automated rigid registration between X-ray CT and FDG-PET. Nuklearmedizin 46: 43–48 PubMedGoogle Scholar
  26. 26.
    Pinheiro JC and Bates M (2000). Mixed-effects models in S and S-PLUS. Springer, New York Google Scholar
  27. 27.
    R Development Core Team (2006) R: A language and environment for statistical computing. R Foundation for statistical Computing, Vienna, Austria. http://www.R-project.org
  28. 28.
    Bates M, Sakar D (2006) lme4: Linear mixed-effects models using S4 classes. R package version 0.995–2. http://CRAN.R-project.org
  29. 29.
    Inagaki H, Kato T and Tadokoro M et al (1999). Interactive fusion of three-dimensional images of upper abdominal CT and FDG PET with no body surface markers. Radiat Med 17: 155–163 PubMedGoogle Scholar
  30. 30.
    Nakamoto Y, Sakamoto S and Okada T et al (2005). Accuracy of image fusion using a fixation device for whole-body cancer imaging. AJR Am J Roentgenol 184: 1960–1966 PubMedGoogle Scholar
  31. 31.
    Skalski J, Wahl RL and Meyer CR (2002). Comparison of mutual information-based warping accuracy for fusing body CT and PET by 2 methods: CT mapped onto PET emission scan versus CT mapped onto PET transmission scan. J Nucl Med 43: 1184–1187 PubMedGoogle Scholar
  32. 32.
    Meyer CR, Boes JL and Kim B et al (1997). Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Med Image Analysis 1: 195–206 CrossRefGoogle Scholar
  33. 33.
    Shekhar R, Walimbe V and Raja S et al (2005). Automated 3-dimensional elastic registration of whole-body PET and CT from separate or combined scanners. J Nucl Med 46: 1488–1496 PubMedGoogle Scholar
  34. 34.
    Mattes D, Haynor DR, Vesselle H, Lewellen TK and Eubank W (2003). PET- CT image registration in the chest using free-form deformations. IEEE Trans Med Imaging 22: 120–128 PubMedCrossRefGoogle Scholar
  35. 35.
    Slomka PJ, Dey D, Przetak C, Aladl UE and Baum RP (2003). Automated 3-dimensonal registration of stand-alone 18F-FDG whole-body PET with CT. J Nucl Med 44: 1156–1167 PubMedGoogle Scholar
  36. 36.
    Krishnasetty V, Fischman AJ, Halpern EL and Aquino SL (2005). Comparison of alignment of computer-registered data sets: combined PET/CT versus independent PET and CT of the thorax. Radiology 237: 635–639 PubMedCrossRefGoogle Scholar
  37. 37.
    Goerres GW, Burger C, Schwitter MR, Heidelberg TN, Seifert B and von Schulthess GK (2003). PET/CT of the abdomen: optimizing the patient breathing pattern. Eur Radiol 13: 734–739 PubMedCrossRefGoogle Scholar
  38. 38.
    West J, Fitzpatrick JM and Wang MY et al (1997). Comparison and evaluation of retrospective intermodality brain image registration techniques. J Comput Assist Tomogr 21: 554–566 PubMedCrossRefGoogle Scholar
  39. 39.
    Gütter C, Xu C, Sauer F et al (2005) Non-rigid multi-modal image registration using Kullback–Leibler divergence MICCAI ’05: Proceedings of the 8th International Conference on Medical Image Computing and Computer Assisted Intervention—Part II, vol 3750. Springer, Heidelberg, pp 255–262Google Scholar
  40. 40.
    Jäger F, Han J, Hornegger J and Kuwert T (2006). A variational approach to spatially dependent non-rigid registration. Proc SPIE 6144: 860–869 Google Scholar

Copyright information

© CARS 2007

Authors and Affiliations

  • Gabriele Wolz
    • 1
  • Anton Nömayr
    • 1
  • Torsten Hothorn
    • 2
  • Joachim Hornegger
    • 3
  • Wolfgang Römer
    • 1
  • Werner Bautz
    • 4
  • Torsten Kuwert
    • 1
  1. 1.Clinic of Nuclear MedicineUniversity of Erlangen/NürnbergErlangenGermany
  2. 2.Department of Medical Informatics, Biometry and EpidemiologyUniversity of Erlangen/NürnbergErlangenGermany
  3. 3.Chair of Pattern RecognitionUniversity of Erlangen/NürnbergErlangenGermany
  4. 4.Institute of RadiologyUniversity of Erlangen/NürnbergErlangenGermany

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