Abstract
Cardiac Magnetic Resonance (CMR) provides unique functional and anatomical visualisation of the macro and micro-structures of the heart. However, CMR acquisition times usually necessitate slices to be acquired at different breath holds, which results in potential misalignment of the acquired slices. Correcting for this spatial misalignment is required for accurate three-dimensional (3D) reconstruction of the heart chambers allowing robust metrics for shape analysis among populations as well as precise representations of individual geometries and scars. While several methods have been proposed to realign slices, their use in other important protocols such as late gadolinium enhancement (LGE) is yet to be demonstrated. We propose a registration framework based on local phase to correct for slice misalignment. Our registration framework is a group registration technique combining long- and short-axis slices. Validation was performed on LGE slices using expert-traced ventricular contours. For 15 clinical multi-breath-hold datasets our method reduced the median discrepancy of moderately misaligned slices from 2.19 mm to 1.63 mm, and of severely misaligned from 7.33 mm to 1.96 mm.
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Acknowledgments
BV acknowledges the support of the RCUK Digital Economy Programme grant number EP/G036861/1 (Oxford Centre for Doctoral Training in Healthcare Innovation). EZ acknowledges the Marie Sklodowska-Curie Individual Fellowship from the H2020 EU Framework Programme for Research and Innovation [Proposal No: 655020-DTI4micro-MSCA-IF-EF-ST]. ED acknowledges the BHF intermediate clinical research fellow grant (FS/13/71/30378) and the NIHR BRC. VG is supported by a BBSRC grant (BB/I012117/1), an EPSRC grant (EP/J013250/1) and by BHF New Horizon Grant NH/13/30238.
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Villard, B., Zacur, E., Dall’Armellina, E., Grau, V. (2017). Correction of Slice Misalignment in Multi-breath-hold Cardiac MRI Scans. In: Mansi, T., McLeod, K., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2016. Lecture Notes in Computer Science(), vol 10124. Springer, Cham. https://doi.org/10.1007/978-3-319-52718-5_4
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