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Local Contractive Registration for Quantification of Tissue Shrinkage in Assessment of Microwave Ablation

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12263))

Abstract

Microwave ablation is an effective minimally invasive surgery for the treatment of liver cancer. The safety margin assessment is implemented by mapping the coagulation in the postoperative image to the tumor in the preoperative image. However, an accurate assessment is a challenging task because the tissue shrinks caused by dehydration during microwave ablation. This paper proposes a fast automatic assessment method to compensate for the underestimation of the coagulation caused by the tissue shrinks and precisely quantify the tumor coverage. The proposed method is implemented on GPU including two main steps: (1) a local contractive nonrigid registration for registering the liver parenchyma around the coagulation, and (2) the fast Fourier transform-based Helmholtz-Hodge decomposition for quantifying the location of the shrinkage center and the volume of the original coagulation. The method was quantificationally evaluated on 50 groups of synthetic datasets and 9 groups of clinical MR datasets. Compared with five state-of-the-art methods, the lowest distance to the true deformation field (1.56 ± 0.74 mm) and the highest precision of safety margin (\(88.89\%\)) are obtained. The mean computation time is \(111\pm 13\) s. Results show that the proposed method efficiently improves the accuracy of the safety margin assessment and is thus a promising assessment tool for the microwave ablation.

Supported by the National Key R&D Program of China (2019YFC0119300).

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References

  1. Ai, D., et al.: Nonrigid registration for tracking incompressible soft tissues with sliding motion. Med. Phys. 46(11), 4923–4939 (2019)

    Article  Google Scholar 

  2. Amabile, C., et al.: Tissue shrinkage in microwave ablation of liver: an ex vivo predictive model. Int. J. Hyperth. 33(1), 101–109 (2017)

    Article  Google Scholar 

  3. Dou, J.P., et al.: Outcomes of microwave ablation for hepatocellular carcinoma adjacent to large vessels: a propensity score analysis. Oncotarget 8(17), 28758 (2017)

    Article  Google Scholar 

  4. Dru, F., Vercauteren, T.: An ITK implementation of the symmetric log-domain diffeomorphic demons algorithm (2009)

    Google Scholar 

  5. Farina, L., Nissenbaum, Y., Cavagnaro, M., Goldberg, S.N.: Tissue shrinkage in microwave thermal ablation: comparison of three commercial devices. Int. J. Hyperth. 34(4), 382–391 (2018)

    Article  Google Scholar 

  6. Farina, L., et al.: Characterisation of tissue shrinkage during microwave thermal ablation. Int. J. Hyperth. 30(7), 419–428 (2014)

    Article  Google Scholar 

  7. Franz, A., et al.: An open-source tool for automated planning of overlapping ablation zones for percutaneous renal tumor treatment. arXiv preprint arXiv:1912.09966 (2019)

  8. Fu, T., et al.: Local incompressible registration for liver ablation surgery assessment. Med. Phys. 44(11), 5873–5888 (2017)

    Article  Google Scholar 

  9. Kim, K.W., et al.: Safety margin assessment after radiofrequency ablation of the liver using registration of preprocedure and postprocedure CT images. Am. J. Roentgenol. 196(5), W565–W572 (2011)

    Article  Google Scholar 

  10. Liu, D., Brace, C.L.: Evaluation of tissue deformation during radiofrequency and microwave ablation procedures: influence of output energy delivery. Med. Phys. 46(9), 4127–4134 (2019)

    Article  Google Scholar 

  11. Luu, H.M., Niessen, W., van Walsum, T., Klink, C., Moelker, A.: An automatic registration method for pre-and post-interventional CT images for assessing treatment success in liver RFA treatment. Med. Phys. 42(9), 5559–5567 (2015)

    Article  Google Scholar 

  12. Malcolm, J., Yalamanchili, P., McClanahan, C., Venugopalakrishnan, V., Patel, K., Melonakos, J.: Arrayfire: a GPU acceleration platform. In: Modeling and Ssimulation for Defense Systems and Applications VII, vol. 8403, p. 84030A. International Society for Optics and Photonics (2012)

    Google Scholar 

  13. Mansi, T., Pennec, X., Sermesant, M., Delingette, H., Ayache, N.: iLogDemons: a demons-based registration algorithm for tracking incompressible elastic biological tissues. Int. J. Comput. Vis. 92(1), 92–111 (2011)

    Article  Google Scholar 

  14. Nyúl, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)

    Article  Google Scholar 

  15. Passera, K., Selvaggi, S., Scaramuzza, D., Garbagnati, F., Vergnaghi, D., Mainardi, L.: Radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through CT image processing. BMC Med. Imaging 13(1), 3 (2013)

    Article  Google Scholar 

  16. Rieder, C., et al.: Automatic alignment of pre-and post-interventional liver CT images for assessment of radiofrequency ablation. In: Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 8316, p. 83163E. International Society for Optics and Photonics (2012)

    Google Scholar 

  17. Solbiati, M., et al.: A novel software platform for volumetric assessment of ablation completeness. Int. J. Hyperth. 36(1), 337–343 (2019)

    Article  Google Scholar 

  18. Tanner, C., et al.: Volume and shape preservation of enhancing lesions when applying non-rigid registration to a time series of contrast enhancing MR breast images. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 327–337. Springer, Heidelberg (2000). https://doi.org/10.1007/978-3-540-40899-4_33

    Chapter  Google Scholar 

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Correspondence to Danni Ai .

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Liu, D., Fu, T., Ai, D., Fan, J., Song, H., Yang, J. (2020). Local Contractive Registration for Quantification of Tissue Shrinkage in Assessment of Microwave Ablation. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_13

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  • DOI: https://doi.org/10.1007/978-3-030-59716-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59715-3

  • Online ISBN: 978-3-030-59716-0

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