Abstract
Cardiac magnetic resonance (CMR) imaging is a valuable imaging technique for the diagnosis and characterisation of cardiovascular diseases. In clinical practice, it is commonly acquired as a collection of separated and independent 2D image planes, limiting its accuracy in 3D analysis. One of the major issues for 3D reconstruction of human heart surfaces from CMR slices is the misalignment between heart slices, often arising from breathing or subject motion. In this regard, the objective of this work is to develop a method for optimal correction of slice misalignments using a statistical shape model (SSM), for accurate 3D modelling of the heart. After extracting the heart contours from 2D cine slices, we perform initial misalignment corrections using the image intensities and the heart contours. Next, our proposed misalignment correction is performed by first optimally fitting an SSM to the sparse heart contours in 3D space and then optimally aligning the heart slices on the SSM, accounting for both in-plane and out-of-plane misalignments. The performance of the proposed approach is evaluated on a cohort of 20 subjects selected from the UK Biobank study, demonstrating an average reduction of misalignment artifacts from \(1.14 \pm 0.23\) mm to \(0.72 \pm 0.11\) mm, in terms of distance from the final reconstructed 3D mesh.
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Acknowledgments
This research has been conducted using the UK Biobank Resource under Application Number ‘40161’. The authors express no conflict of interest. The work was supported by the British Heart Foundation Project under Grant HSR01230 and the CompBioMed 2 Centre of Excellence in Computational Biomedicine (European Commission Horizon 2020 research and innovation programme, grant agreement No. 823712). The authors acknowledge the use of services and facilities of the Institute of Biomedical Engineering, University of Oxford, UK and the Oxford Acute Vascular Imaging Centre, UK.
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Banerjee, A., Zacur, E., Choudhury, R.P., Grau, V. (2021). Optimised Misalignment Correction from Cine MR Slices Using Statistical Shape Model. In: Papież, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds) Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science(), vol 12722. Springer, Cham. https://doi.org/10.1007/978-3-030-80432-9_16
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DOI: https://doi.org/10.1007/978-3-030-80432-9_16
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