Abstract
Data assimilation in computational models represents an essential step in building patient-specific simulations. This work aims at circumventing one major bottleneck in the practical use of data assimilation strategies in cardiac applications, namely, the difficulty of formulating and effectively computing adequate data-fitting term for cardiac imaging such as cine MRI. We here provide a proof-of-concept study of data assimilation based on automatic contour detection. The tissue motion simulated by the data assimilation framework is then assessed with displacements extracted from tagged MRI in six subjects, and the results illustrate the performance of the proposed method, including for circumferential displacements, which are not well extracted from cine MRI alone.
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Acknowlegement
The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London and the NIHR Cardiovascular MedTech Co-operative (previously existing as the Cardiovascular Healthcare Technology Co-operative 2012–2017), the support of Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
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Chabiniok, R. et al. (2019). Cardiac Displacement Tracking with Data Assimilation Combining a Biomechanical Model and an Automatic Contour Detection. In: Coudière, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds) Functional Imaging and Modeling of the Heart. FIMH 2019. Lecture Notes in Computer Science(), vol 11504. Springer, Cham. https://doi.org/10.1007/978-3-030-21949-9_44
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DOI: https://doi.org/10.1007/978-3-030-21949-9_44
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