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Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering
Author(s) -
Shi Lin,
Wang Defeng,
Liu Wen,
Fang Kui,
Wang YiXiang J.,
Huang Wenhua,
King Ann D.,
Heng Pheng Ann,
Ahuja Anil T.
Publication year - 2014
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.24704
Subject(s) - initialization , computer science , robustness (evolution) , cluster analysis , affinity propagation , contrast (vision) , artificial intelligence , pattern recognition (psychology) , dynamic contrast , magnetic resonance imaging , chemistry , fuzzy clustering , radiology , medicine , canopy clustering algorithm , biochemistry , gene , programming language
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 .