Abstract
Inter-subject registration of cardiac images is a vital yet challenging task due to the large deformations influenced by the cardiac cycle and respiration. Various intensity-based cardiac registration methods have already been proposed, but such methods utilize intensity information over the entire image domain and are thus computationally expensive. In this work, we propose a novel pipeline for fast registration of cardiac MR images that relies on shape priors and the strategic location of surface-approximating landmarks. Our holistic approach to cardiac registration requires minimal user input. It also reduces the computational runtime by \(60\%\) on average, which amounts to an 11-min speedup in runtime. Most importantly, the resulting Dice similarity coefficients are comparable to those from a widely used elastic registration method.
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Acknowledgments
This work was supported in part by a Natural Sciences and Engineering Research Council of Canada (NSERC) grant for Dr. Mehran Ebrahimi and a Canadian Institutes of Health Research (CIHR) project grant for Dr. Mihaela Pop. Mia Mojica is supported by an Ontario Trillium Scholarship (OTS).
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Mojica, M., Pop, M., Ebrahimi, M. (2021). Automatic Detection of Landmarks for Fast Cardiac MR Image Registration. In: Puyol Anton, E., et al. Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science(), vol 12592. Springer, Cham. https://doi.org/10.1007/978-3-030-68107-4_9
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DOI: https://doi.org/10.1007/978-3-030-68107-4_9
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