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Cine and Multicontrast Late Enhanced MRI Registration for 3D Heart Model Construction

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Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges (STACOM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11395))

Abstract

Cardiac MR imaging using multicontrast late enhancement (MCLE) acquisition provides a way to identify myocardium infarct scar and arrhythmia foci in the peri-infarct. In image-guided RF ablations of ventricular arrhythmia and computational modeling of cardiac function, construction of a 3D heart model is required but this is hampered by the challenges in myocardium segmentation and slice misalignment in MCLE images. Here we developed an approach for cine and MCLE registration, and MCLE scar-cine myocardium label fusion to build high-fidelity 3D heart models. MCLE-cine image alignment was initialized using a block-matching-based rigid registration approach followed by a deformable registration refinement step. The deformable registration approach employed a self similarity context descriptor for image similarity measurements, optical flow as a transformation model and convex optimization to derive the optimal solution. We applied the developed approach to a preclinical dataset of 10 pigs with myocardium infarction and evaluated the registration accuracy by comparing cine and MCLE myocardium masks using Dice-similarity-coefficient (DSC) and average symmetric surface distance (ASSD). For 10 pigs, we achieved a mean DSC of \(80.4\pm 7.8\)% and ASSD of \(1.28\pm 0.47\) mm for myocardium with a mean runtime of 1.5 min for each dataset. These results suggest that the developed approach provide the registration accuracy and computational efficiency that may be suitable for clinical applications of cardiac MRI that involve a cine and MCLE MRI registration component.

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References

  1. Bertsekas, D.P.: Nonlinear Programming. Athena Scientific, Belmont (1999)

    MATH  Google Scholar 

  2. Chenoune, Y., et al.: Rigid registration of delayed-enhancement and cine cardiac MR images using 3D normalized mutual information. In: Computing in Cardiology, pp. 161–164. IEEE (2010)

    Google Scholar 

  3. Detsky, J.S., Paul, G., Dick, A.J., Wright, G.A.: Reproducible classification of infarct heterogeneity using fuzzy clustering on multicontrast delayed enhancement magnetic resonance images. IEEE Trans. Med. Imaging 28(10), 1606–1614 (2009)

    Article  Google Scholar 

  4. Detsky, J., Stainsby, J., Vijayaraghavan, R., Graham, J., Dick, A., Wright, G.: Inversion-recovery-prepared SSFP for cardiac-phase-resolved delayed-enhancement MRI. Magn. Reson. Med. 58(2), 365–372 (2007)

    Article  Google Scholar 

  5. Guo, F., Capaldi, D.P., Di Cesare, R., Fenster, A., Parraga, G.: Registration pipeline for pulmonary free-breathing 1 H MRI ventilation measurements. In: Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 10137, p. 101370A. International Society for Optics and Photonics (2017)

    Google Scholar 

  6. Guo, F., et al.: Thoracic CT-MRI coregistration for regional pulmonary structure-function measurements of obstructive lung disease. Med. Phys. 44(5), 1718–1733 (2017)

    Article  Google Scholar 

  7. Heinrich, M.P., Jenkinson, M., Papież, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187–194. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_24

    Chapter  Google Scholar 

  8. Lucas, B.D., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision (1981)

    Google Scholar 

  9. Ourselin, S., Roche, A., Prima, S., Ayache, N.: Block matching: a general framework to improve robustness of rigid registration of medical images. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 557–566. Springer, Heidelberg (2000). https://doi.org/10.1007/978-3-540-40899-4_57

    Chapter  Google Scholar 

  10. Pop, M., et al.: Quantification of fibrosis in infarcted swine hearts by ex vivo late gadolinium-enhancement and diffusion-weighted MRI methods. Phys. Med. Biol. 58(15), 5009 (2013)

    Article  Google Scholar 

  11. Tao, Q., Piers, S.R., Lamb, H.J., van der Geest, R.J.: Automated left ventricle segmentation in late gadolinium-enhanced mri for objective myocardial scar assessment. J. Magn. Reson. Imaging 42(2), 390–399 (2015)

    Article  Google Scholar 

  12. Wei, D., Sun, Y., Chai, P., Low, A., Ong, S.H.: Myocardial segmentation of late gadolinium enhanced MR images by propagation of contours from cine MR images. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 428–435. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23626-6_53

    Chapter  Google Scholar 

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Correspondence to Fumin Guo .

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Guo, F., Li, M., Ng, M., Wright, G., Pop, M. (2019). Cine and Multicontrast Late Enhanced MRI Registration for 3D Heart Model Construction. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science(), vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-12029-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12028-3

  • Online ISBN: 978-3-030-12029-0

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