@article{117b3ecec4164bd8ab52fd65530b267f,

title = "Hemodynamic Segmentation of Brain Perfusion Images with Delay and Dispersion Effects Using an Expectation-Maximization Algorithm",

abstract = "Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles to discern areas of different tissues. However, the cerebrovascular diseases may result in a delayed and dispersed local perfusion and therefore alter the hemodynamic signal profiles. Assessing the accuracy of the segmentation technique under delayed/dispersed circumstance is critical to accurately evaluate the severity of the vascular disease. In this study, we improved the segmentation method of expectation-maximization algorithm by using the results of hierarchical clustering on whitened perfusion data as initial parameters for a mixture of multivariate Gaussians model. In addition, Monte Carlo simulations were conducted to evaluate the performance of proposed method under different levels of delay, dispersion, and noise of signal profiles in tissue segmentation. The proposed method was used to classify brain tissue types using perfusion data from five normal participants, a patient with unilateral stenosis of the internal carotid artery, and a patient with moyamoya disease. Our results showed that the normal, delayed or dispersed hemodynamics can be well differentiated for patients, and therefore the local arterial input function for impaired tissues can be recognized to minimize the error when estimating the cerebral blood flow. Furthermore, the tissue in the risk of infarct and the tissue with or without the complementary blood supply from the communicating arteries can be identified. {\textcopyright} 2013 Lu et al.",

keywords = "adult, aged, algorithm, article, brain blood flow, brain infarction, brain perfusion, brain tissue, case report, cluster analysis, controlled study, dispersion, expectation maximization algorithm, female, hemodynamics, human, internal carotid artery occlusion, kernel method, male, Monte Carlo method, moyamoya disease, tissue section, Adolescent, Adult, Aged, Algorithms, Brain, Carotid Artery, Internal, Carotid Stenosis, Cerebrovascular Circulation, Computer Simulation, Female, Hemodynamics, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Monte Carlo Method, Moyamoya Disease, Perfusion Imaging, Young Adult",

author = "Chia-Feng Lu and Wan-Yuo Guo and Feng-Chi Chang and Shang-Ran Huang and Yen-Chun Chou and Yu-Te Wu",

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year = "2013",

doi = "10.1371/journal.pone.0068986",

language = "English",

volume = "8",

journal = "PLoS One",

issn = "1932-6203",

publisher = "Public Library of Science",

number = "7",

}