ArticlePloS one2014
Evaluating the feasibility of an agglomerative hierarchy clustering algorithm for the automatic detection of the arterial input function using DSC-MRI.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- TriAIF-RWKV: A Physiology-Guided Spatiotemporal Framework for Robust Arterial Input Function Selection in CT Perfusion Imaging.Entropy (Basel, Switzerland) · 2026Article
- Relationship Between RAP and Multi-Modal Cerebral Physiological Dynamics in Moderate/Severe Acute Traumatic Neural Injury: A CAHR-TBI Multivariate Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors.Journal of biomedical physics & engineering · 2019Article
- Evaluation of an automated method for arterial input function detection for first-pass myocardial perfusion cardiovascular magnetic resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2016Article
- Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering.PloS one · 2015Article
- Extended emitter target tracking using GM-PHD filter.PloS one · 2014Article
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3 authors.
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Abstract
During dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI), it has been demonstrated that the arterial input function (AIF) can be obtained using fuzzy c-means (FCM) and k-means clustering methods. However, due to the dependence on the initial centers of clusters, both clustering methods have poor reproducibility between the calculation and recalculation steps. To address this problem, the present study developed an alternative clustering technique based on the agglomerative hierarchy (AH) method for AIF determination. The performance of AH method was evaluated using simulated data and clinical data based on comparisons with the two previously demonstrated clustering-based methods in terms of the detection accuracy, calculation reproducibility, and computational complexity. The statistical analysis demonstrated that, at the cost of a significantly longer execution time, AH method obtained AIFs more in line with the expected AIF, and it was perfectly reproducible at different time points. In our opinion, the disadvantage of AH method in terms of the execution time can be alleviated by introducing a professional high-performance workstation. The findings of this study support the feasibility of using AH clustering method for detecting the AIF automatically.
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