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Multimodality liver registration of Open-MR and CT scans

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

Abstract

Purpose

Multimodality registration of liver CT and MRI scans is challenging due to large initial misalignment, non-uniform MR signal intensity in the liver parenchyma, incomplete liver shapes in Open-MR scans and non-rigid deformations of the organ. An automated method was developed to register liver CT and open-MRI scans.

Methods

A hybrid registration algorithm was developed which incorporates both rigid and non-rigid methods. First, large misalignment of input CT and Open-MR images was compensated by intensity-based registration. Maximum intensity projections (MIPs) of CT and MR data were registered in 2D, and the corresponding rigid transform parameters were used to align 3D images in axial, coronal and sagittal planes. Use of MIP projections compensates for intensity inhomogeneities inherent in the Open-MR data. A bounding box of MIP images defines an ROI which removes outliers and copes with incomplete MR data. Next, principal components analysis (PCA) was used to align MR and CT data datasets. The corresponding translation and rotation parameters were then used to increase the global registration accuracy. A modified TPS-RPM point-based non-rigid algorithm was used to accommodate local liver deformations. Surface points on the liver and branching points of the portal veins were input as landmarks to TPS-RPM method. Incorporating vascular branching points improves registration since tumors are usually found near vessels, so greater weight was given to branching points compared with surface points.

Results

The automated registration algorithm was compared with both rigid and non-rigid methods. Quantitative evaluation was performed using modified Hausdorff distance and overlap measure. The mean modified Hausdorff distances of liver and tumor were decreased from 23.53 and 40.03 mm to 9.38 and 8.88 mm, respectively. The mean overlap measures of liver and tumor were increased from 39 and 0 % to 78 and 27 %, respectively. Statistical analysis of the outcomes resulted in a p value less than 5 %.

Conclusion

MIP-PCA-based rigid multimodality CT–MRI registration of liver scans compensates for large misalignment of input images even when the data are incomplete. A modified TPS-RPM algorithm, in which vascular points are emphasized over surface points, successfully handled local deformations.

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References

  1. Grey ML, Ailinani JM (2003) CT & MRI pathology: a pocket atlas. McGraw-Hill Medical, New York

    Google Scholar 

  2. Morikawa S, Inubushi T, Kurumi Y, Naka S, Sato K, Demura K, Tani T, Haque HA, Tokuda J, Hata N (2003) Advanced computer assistance for magnetic resonance-guided microwave thermocoagulation of liver tumors. Acad Radiol 10(12):1442–1449

    Article  PubMed  Google Scholar 

  3. Nakayama Y, Li Q, Katsuragawa S, Ikeda R, Hiai Y, Awai K, Kusunoki S, Yamashita Y, Okajima H, Inomata Y, Doi K (2006) Automated hepatic volumetry for living related liver transplantation at multisection CT. Radiology 240(3):743-748

  4. Selle D, Preim B, Schenk A, Peitgen HO (2002) Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging 21(11):1344–1357

    Article  PubMed  Google Scholar 

  5. Meinzer HP, Schemmer P, Schöbinger M, Nolden M, Heimann T, Yalcin B, Richte GM, Kraus T, Buchler MW, Thorn M (2004) Computer-based surgery planning for living liver donation. In: 20th ISPRS Congress, Istanbul, pp 291–295

  6. Huang X, Wang B, Liu R, Wang X, Wu Z (2008) CT-MR image registration in liver treatment by maximization of mutual information. In: IEEE international symposium onIT in medicine and education, 2008. ITME 2008. pp 715–718

  7. Orchard J (2007) Efficient least squares multimodal registration with a globally exhaustive alignment search. IEEE Trans Image Process 16(10):2526–2534

    Article  PubMed  Google Scholar 

  8. Zhu YM (2012) Volume sweeping and bodyline matching for automated prealignment in volumetric medical image registration. Comput Biol Med 42(11):1043–1052

    Article  PubMed  Google Scholar 

  9. Tang S, Wang Y (2010) MR-guided liver cancer surgery by nonrigid registration. In: 2010 international conference on medical image analysis and clinical applications (MIACA). pp 113–117

  10. Lee D, Nam WH, Lee JY, Ra JB (2011) Non-rigid registration between 3D ultrasound and CT images of the liver based on intensity and gradient information. Phys Med Biol 56(1):117

    Article  PubMed  Google Scholar 

  11. Gering DT, Nabavi A, Kikinis R, Hata N, O’Donnell LJ, Grimson WEL, Jolesz FA, Black PM, Wells WM (2001) An integrated visualization system for surgical planning and guidance using image fusion and an open MR. J Magn Reson Imaging 13(6):967–975

    Article  CAS  PubMed  Google Scholar 

  12. Rieder C, Wirtz S, Strehlow J, Zidowitz S, Bruners P, Isfort P, Mahnken AH, Peitgen HO (2012) Automatic alignment of pre-and post-interventional liver CT images for assessment of radiofrequency ablation. In: SPIE Medical Imaging, international society for optics and photonics SPIE medical imaging. p 83163E

  13. Zhang X, Fujita H, Qin T, Zhao J, Kanematsu M, Hara T, Zhou X, Yokoyama R, Kondo H, Hoshi H (2008) CAD on liver using CT and MRI. In: Medical imaging and informatics. Springer, Berlin, pp 367–376

  14. Alpert NM, Bradshaw JF, Kennedy D, Correia JA (1990) The principal axes transformation —a method for image registration. J Nucl Med 31:1717–1723

  15. Fernandez-de-Manuel L, Wollny G, Kybic J, Jimenez-Carretero D, Tellado JM, Ramon E, Desco M, Santos A, Pascau J, Ledesma-Carbayo MJ (2014) Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd-EOB-DTPA-enhanced MRI. Med Image Anal 18(1):22–35

    Article  PubMed  Google Scholar 

  16. Song H, Li JJ, Wang SL, Ma JT (2014) Multi-modality liver image registration based on multilevel B-splines free-form deformation and L-BFGS optimal algorithm. J Cent South Univ 21:287–292

    Article  Google Scholar 

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

    Article  PubMed Central  PubMed  Google Scholar 

  18. Chen YW, Xu R, Tang SY, Morikawa S, Kurumi Y (2007) Non-rigid MR-CT image registration for MR-guided liver cancer surgery. In: IEEE/ICME international conference on complex medical engineering, 2007. CME 2007. pp 1756–1760

  19. Chui H, Rangarajan A (2003) A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding 89(2):114–141

    Article  Google Scholar 

  20. Foruzan AH, Yen-Wei CHEN, Zoroofi RA, Furukawa A, Masatoshi HORI, Tomiyama N (2013) Segmentation of liver in low-contrast images using K-means clustering and geodesic active contour algorithms. IEICE Trans Inf Syst 96(4):798–807

    Article  Google Scholar 

  21. Smith LI (2002) A tutorial on principal components analysis, vol 51. Cornell University, USA

    Google Scholar 

  22. Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. In: ACM siggraph computer graphics, vol 21, No 4, pp 163–169. ACM

  23. Garland M, Heckbert PS (1997) Surface simplification using quadric error metrics. In: Proceedings of the 24th annual conference on computer graphics and interactive techniques, pp 209–216. ACM Press/Addison-Wesley Publishing Co

  24. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Medical image computing and computer-assisted interventation—MICCAI’98. Springer, Berlin, pp 130–137

  25. Lee TC, Kashyap RL, Chu CN (1994) Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP Graph Models Image Process 56(6):462–478

    Article  Google Scholar 

  26. Available: http://www.ITKSnap.org. Accessed: 30 May, 2014

  27. 3D Slicer. http://www.Slicer.org/. Accessed: 20 March 2014

  28. Dubuisson MP, Jain AK (1994) A modified Hausdorff distance for object matching. In: Proceedings of the 12th IAPR international conference on pattern recognition, vol 1-conference A: computer vision and image processing, vol. 1, pp 566–568

  29. Niculescu G, Foran DJ, Nosher J (2007) Non-rigid registration of the liver in consecutive CT studies for assessment of tumor response to radiofrequency ablation. In: 29th Annual international conference of the IEEE, engineering in medicine and biology society, 2007. EMBS 2007. pp 856–859

  30. Besl PJ, McKay ND (1992) Method for registration of 3-D shapes. In: Robotics-DL tentative. International society for optics and photonics, pp 586–606

  31. Insight toolkit. http://www.ITK.org/. Accessed: 20 March 2014

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Acknowledgments

The authors would like to thank Prof. Yen-Wei Chen, Ritsumeikan University, Shiga, Japan, and Prof. Yoshinobu Sato and Prof. Masatoshi Hori, Osaka University, Osaka, Japan, for the use of their images in this study.

Conflict of interest

Amir Hossein Foruzan and Hossein Rajabzadeh Motlagh declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran

    Amir Hossein Foruzan & Hossein Rajabzadeh Motlagh

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  1. Amir Hossein Foruzan
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  2. Hossein Rajabzadeh Motlagh
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Correspondence to Amir Hossein Foruzan.

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Foruzan, A.H., Motlagh, H.R. Multimodality liver registration of Open-MR and CT scans. Int J CARS 10, 1253–1267 (2015). https://doi.org/10.1007/s11548-014-1139-0

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  • DOI: https://doi.org/10.1007/s11548-014-1139-0

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