Abstract
The short-axis view defined such that a series of slices are perpendicular to the long-axis of the left ventricle (LV) is one of the most important views in cardiovascular imaging. Raw trans-axial Computed Tomography (CT) images must be often reformatted prior to diagnostic interpretation in short-axis view. The clinical importance of this reformatting requires the process to be accurate and reproducible. It is often performed after manual localization of landmarks on the image (e.g. LV apex, centre of the mitral valve, etc.) being slower and not fully reproducible as compared to automatic approaches. We propose a fast, automatic and reproducible method to reformat CT images from original trans-axial orientation to short-axis view. A deep learning based segmentation method is used to automatically segment the LV endocardium and wall, and the right ventricle epicardium. Surface meshes are then obtained from the corresponding masks and used to automatically detect the shape features needed to find the transformation that locates the cardiac chambers on their standard, mathematically defined, short-axis position. 25 datasets with available manual reformatting performed by experienced cardiac radiologists are used to show that our reformatted images are of equivalent quality.
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Acknowledgements
Part of this work was funded by the ERC starting grant EC-STATIC (715093), the IHU LIRYC (ANR-10-IAHU-04), the Equipex MUSIC (ANR-11-EQPX-0030) and the ANR ERACoSysMed SysAFib projects. This work was also supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002. We would like to thank all patients who agreed to make available their clinical data for research.
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Nuñez-Garcia, M., Cedilnik, N., Jia, S., Sermesant, M., Cochet, H. (2021). Automatic Multiplanar CT Reformatting from Trans-Axial into Left Ventricle Short-Axis View. 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_2
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