ArticleNeuroradiology2015
Automatic determination of the arterial input function in dynamic susceptibility contrast MRI: comparison of different reproducible clustering algorithms.
Article in Neuroradiology, 2015. 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.
- Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics.Annals of biomedical engineering · 2024Article
- Article
- Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics.Magnetic resonance imaging · 2022Article
- Perfusion weighted imaging using combined gradient/spin echo EPIK: Brain tumour applications in hybrid MR-PET.Human brain mapping · 2021Article
- An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors.Journal of biomedical physics & engineering · 2019Article
- Semiautomatic determination of arterial input function in DCE-MRI of the abdomen.Journal of biomedical engineering and medical imaging · 2017Article
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Authors and funding
3 authors.
Funding
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Abstract
introductionArterial input function (AIF) plays an important role in the quantification of cerebral hemodynamics. The purpose of this study was to select the best reproducible clustering method for AIF detection by comparing three algorithms reported previously in terms of detection accuracy and computational complexity.
methodsFirst, three reproducible clustering methods, normalized cut (Ncut), hierarchy (HIER), and fast affine propagation (FastAP), were applied independently to simulated data which contained the true AIF. Next, a clinical verification was performed where 42 subjects participated in dynamic susceptibility contrast MRI (DSC-MRI) scanning. The manual AIF and AIFs based on the different algorithms were obtained. The performance of each algorithm was evaluated based on shape parameters of the estimated AIFs and the true or manual AIF. Moreover, the execution time of each algorithm was recorded to determine the algorithm that operated more rapidly in clinical practice.
resultsIn terms of the detection accuracy, Ncut and HIER method produced similar AIF detection results, which were closer to the expected AIF and more accurate than those obtained using FastAP method; in terms of the computational efficiency, the Ncut method required the shortest execution time.
conclusionNcut clustering appears promising because it facilitates the automatic and robust determination of AIF with high accuracy and efficiency.
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