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- W1896978175 abstract "Purpose To automatically and robustly detect the arterial input function (AIF) with high detection accuracy and low computational cost in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and Methods In this study, we developed an automatic AIF detection method using an accelerated version (Fast-AP) of affinity propagation (AP) clustering. The validity of this Fast-AP-based method was proved on two DCE-MRI datasets, i.e., rat kidney and human head and neck. The detailed AIF detection performance of this proposed method was assessed in comparison with other clustering-based methods, namely original AP and K-means, as well as the manual AIF detection method. Results Both the automatic AP- and Fast-AP-based methods achieved satisfactory AIF detection accuracy, but the computational cost of Fast-AP could be reduced by 64.37–92.10% on rat dataset and 73.18–90.18% on human dataset compared with the cost of AP. The K-means yielded the lowest computational cost, but resulted in the lowest AIF detection accuracy. The experimental results demonstrated that both the AP- and Fast-AP-based methods were insensitive to the initialization of cluster centers, and had superior robustness compared with K-means method. Conclusion The Fast-AP-based method enables automatic AIF detection with high accuracy and efficiency. J. Magn. Reson. Imaging 2014;39:1327–1337. © 2013 Wiley Periodicals, Inc." @default.
- W1896978175 created "2016-06-24" @default.
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- W1896978175 date "2013-10-10" @default.
- W1896978175 modified "2023-10-03" @default.
- W1896978175 title "Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering" @default.
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- W1896978175 doi "https://doi.org/10.1002/jmri.24259" @default.
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