Abstract
Every Siemens Magnetic Resonance Imaging (MRI) system consistently writes events into log files while the system is running. The log files and their contents are constantly refined by software developers. This results in different information contents depending on the software version. One information that is missing in some log files is the examined body region. As the body region is crucial for usage analysis, we used pattern recognition methods to estimate the examined body region for software versions not logging it automatically. We learned the examined body region from a set of used MRI acquisition parameters such as grid and voxel size and could classify body region information with a classification rate up to \(94.7\%\). We compared Bayesian Network augmented Naïve Bayes, Decision Trees, and Neural Networks, and found Neural Networks resulting in the best classification rate.
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Kuhnert, N., Lindenmayr, O., Maier, A. (2018). Classification of Body Regions Based on MRI Log Files. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_11
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DOI: https://doi.org/10.1007/978-3-319-59162-9_11
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