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Evaluation of bivariate correlation ratio similarity metric for rigid registration of US/MR images of the liver

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objective

Radio frequency ablation (RFA) can be used to treat liver cancer minimally invasively by depositing energy from the RF probe placed in the center of the tumor. The procedure relies on pre-operative imaging (typically MRI or CT) for the interventional planning and ultrasound (US) for intra-operative guidance during needle insertion. Visual presentation of co-registered pre- and intra-operative images would help to improve the navigation during the needle positioning phase.

Methods

In the present study, we compared six registration methods using different similarity metrics: two versions of the correlation ratio, bivariate correlation ratio, and conventional normalized mutual information and correlation coefficient. The accuracy, robustness and speed were assessed by computing rigid registrations between eight pairs of the MR and freehand 3D US datasets.

Results

The correlation ratio computed on the MR-gradient-norm and US images outperformed other similarity metrics in terms of robustness (40–82%) and demonstrated average accuracy (0.32°, 0.69 mm) which is clinically acceptable for the RFA of liver cancer.

Conclusions

We observed that the performance of all similarity metrics is largely dependent on the quality of the US images, sufficient field of view of the reconstructed 3D US and absence of motion artifacts.

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References

  1. Mellor M, Brady M (2004) Non-rigid multimodal image registration using local phase. Lect Notes Comput Sci 3216: 789–796

    Google Scholar 

  2. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16(2): 187–198. doi:10.1109/42.563664

    Article  PubMed  CAS  Google Scholar 

  3. Viola P, Wells WM (1997) Alignment by maximization of mutual information. Int J Comput Vis 24(2): 137–154. doi:10.1023/A:1007958904918

    Article  Google Scholar 

  4. Penney GP, Blackall JM, Hamady MS, Sabharwal T, Adam A, Hawkes DJ (2004) Registration of freehand 3D ultrasound and magnetic resonance liver images. Med Image Anal 8(1): 81–91. doi:10.1016/j.media.2003.07.003

    Article  PubMed  CAS  Google Scholar 

  5. 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(5): 577–585. doi:10.1016/j.media.2008.06.006

    Article  PubMed  Google Scholar 

  6. Lee D, Kim YS, Lee JH, Ra JB (2008) Non-rigid registration of 3D ultrasound and CT images in the liver using intensity and gradient information. In: proceedings of CARS 2008, Barcelona, Spain

  7. Roche A, Pennec X, Malandain G, Ayache N (2001) Rigid registration of 3D ultrasound with MR images: a new approach combining intensity and gradient information. IEEE Trans Med Imaging 20(10): 1038–1049. doi:10.1109/42.959301

    Article  PubMed  CAS  Google Scholar 

  8. Roche A, Pennec X, Malandain G, Ayache N, Ourselin S (2000) Generalized correlation ratio for rigid registration of 3-D ultrasound with MR images, INRIA, Tech. Rep. 3980

  9. Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity measure for multimodal image registration. Lect Notes Comput Sci 1496: 1115–1124. doi:10.1007/BFb0056301

    Article  Google Scholar 

  10. Hill DLG, Batchelor PG, Holden M, Hawkes DJ (2001) Medical image registration. Phys Med Biol 46(3): R1–R45. doi:10.1088/0031-9155/46/3/201

    Article  PubMed  CAS  Google Scholar 

  11. Hajnal JV, Hill DLG, Hawkes DJ (2001) Medical image registration. CRC Press LLC, Boca Raton. ISBN 0-8493-0064-9

  12. Milko S, McLaughlin RA (2008) Evaluation of generalized correlation ratio similarity metric for rigid registration of US/MR images of the liver. In: Proceedings of CARS 2008, Barcelona, Spain

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Correspondence to Sergiy Milko.

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Milko, S., Melvær, E.L., Samset, E. et al. Evaluation of bivariate correlation ratio similarity metric for rigid registration of US/MR images of the liver. Int J CARS 4, 147–155 (2009). https://doi.org/10.1007/s11548-009-0285-2

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  • DOI: https://doi.org/10.1007/s11548-009-0285-2

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