Automatic determination of the arterial input function in dynamic susceptibility contrast MRI: comparison of different reproducible clustering algorithms

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• 作者：Jiandong Yin ; Jiawen Yang ; Qiyong Guo
• 关键词：Affine propagation ; Arterial input function ; Cerebral perfusion ; Normalized cut
• 刊名：Neuroradiology
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：57
• 期：5
• 页码：535-543
• 全文大小：1,351 KB
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• 作者单位：Jiandong Yin (1)
  Jiawen Yang (1)
  Qiyong Guo (1)
  
  1. Department of Radiology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004, People鈥檚 Republic of China
  
• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  Neuroradiology
  Imaging and Radiology
  Neurology
  Neurosurgery
  Neurosciences
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-1920

文摘

Introduction Arterial 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. Methods First, 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. Results In 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. Conclusion Ncut clustering appears promising because it facilitates the automatic and robust determination of AIF with high accuracy and efficiency.