Abstract
Biomedical research, combining multi-modal image and geometry data, presents unique challenges for data visualization, processing, and quantitative analysis. Medical imaging provides rich information, from anatomical to deformation, but extracting this to a coherent picture across image modalities with preserved quality is not trivial. Addressing these challenges and integrating visualization with image and quantitative analysis results in Eidolon, a platform which can adapt to rapidly changing research workflows. In this paper we outline Eidolon, a software environment aimed at addressing these challenges, and discuss the novel integration of visualization and analysis components. These capabilities are demonstrated through the example of cardiac strain analysis, showing the Eidolon supports and enhances the workflow.
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Acknowledgements
This research was partly supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC), and by the NIHR Healthcare Technology Co-operative for Cardiovascular Disease, both at Guy’s and St Thomas’ NHS Foundation Trust. The research leading to these results has received funding from the EU FP7 for research, technological development and demonstration under grant agreement VP2HF (no 611823), and from BHF New Horizons grant NH/11/5/29058. Views expressed are those of the authors and not necessarily of the NHS, the BHF, the NIHR, or the Department of Health.
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© 2016 Springer International Publishing Switzerland
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Kerfoot, E. et al. (2016). Eidolon: Visualization and Computational Framework for Multi-modal Biomedical Data Analysis. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, SL. (eds) Medical Imaging and Augmented Reality. MIAR 2016. Lecture Notes in Computer Science(), vol 9805. Springer, Cham. https://doi.org/10.1007/978-3-319-43775-0_39
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DOI: https://doi.org/10.1007/978-3-319-43775-0_39
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