## 摘要

原文 | 英語 |
---|---|

期刊 | PLoS One |

卷 | 8 |

發行號 | 7 |

DOIs | |

出版狀態 | 已發佈 - 2013 |

對外發佈 | 是 |

## UN SDG

此研究成果有助於以下永續發展目標

## 存取文件

## 其它文件與鏈接

## 指紋

深入研究「Hemodynamic Segmentation of Brain Perfusion Images with Delay and Dispersion Effects Using an Expectation-Maximization Algorithm」主題。共同形成了獨特的指紋。## 引用此

- APA
- Standard
- Harvard
- Vancouver
- Author
- BIBTEX
- RIS

*PLoS One*,

*8*(7). https://doi.org/10.1371/journal.pone.0068986

**Hemodynamic Segmentation of Brain Perfusion Images with Delay and Dispersion Effects Using an Expectation-Maximization Algorithm.**/ Lu, Chia-Feng; Guo, Wan-Yuo; Chang, Feng-Chi 等.

於: PLoS One, 卷 8, 編號 7, 2013.

研究成果: 雜誌貢獻 › 文章 › 同行評審

*PLoS One*, 卷 8, 編號 7. https://doi.org/10.1371/journal.pone.0068986

**Hemodynamic Segmentation of Brain Perfusion Images with Delay and Dispersion Effects Using an Expectation-Maximization Algorithm**. 於: PLoS One. 2013 ; 卷 8, 編號 7.

}

TY - JOUR

T1 - Hemodynamic Segmentation of Brain Perfusion Images with Delay and Dispersion Effects Using an Expectation-Maximization Algorithm

AU - Lu, Chia-Feng

AU - Guo, Wan-Yuo

AU - Chang, Feng-Chi

AU - Huang, Shang-Ran

AU - Chou, Yen-Chun

AU - Wu, Yu-Te

N1 - 被引用次數：2 Export Date: 31 March 2016 CODEN: POLNC 通訊地址: Wu, Y.-T.; Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan; 電子郵件： [email protected] 參考文獻: Lassen, N.A., Perl, W., (1979) Tracer kinetics methods in medical physiology, , New York: Raven press; Zierler, K.L., Equations for measuring blood flow by external monitoring of radioisotopes (1965) Circulation Research, 16, pp. 309-321; Aronen, H.J., Glass, J., Pardo, F.S., Belliveau, J.W., Gruber, M.L., Echo-planar MR cerebral blood volume mapping of gliomas (1995) Acta Radiologica, 36, pp. 520-528; Ostergaard, L., Sorensen, A.G., Kwong, K.K., Weisskoff, R.M., Gyldensted, C., High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: Experimental comparison and preliminary results (1996) Magnetic Resonance in Medicine, 36, pp. 726-736; Sorensen, A.G., Tievsky, A.L., Ostergaard, L., Weisskoff, R.M., Rosen, B.R., Contrast agents in functional MR imaging (1997) Journal of Magnetic Resonance Imaging, 7, pp. 47-55; Kane, I., Carpenter, T., Chappell, F., Rivers, C., Armitage, P., Comparison of 10 Different Magnetic Resonance Perfusion Imaging Processing Methods in Acute Ischemic Stroke (2007) Stroke, 38, pp. 3158-3164; Rosen, B.R., Belliveau, J.W., Vevea, J.M., Brady, T.J., Perfusion imaging with NMR contrast agents (1990) Magnetic Resonance in Medicine, 14, pp. 249-265; Guckel, F.J., Brix, G., Schmiedek, P., Piepgras, Z., Becker, G., Cerebrovascular reserve capacity in patients with occlusive cerebrovascular disease: assessment with dynamic susceptibility contrast-enhanced MR imaging and the acetazolamide stimulation test (1996) Radiology, 201, pp. 405-412; Guo, W.Y., Wu, Y.T., Wu, H.M., Chung, W.Y., Kao, Y.H., Toward Normal Perfusion after Radiosurgery: Perfusion MR Imaging with Independent Component Analysis of Brain Arteriovenous Malformations (2004) American Journal of Neuroradiology, 25, pp. 1636-1644; Kao, Y.H., Guo, W.Y., Wu, Y.T., Liu, K.C., Chai, W.Y., Hemodynamic segmentation of MR brain perfusion images using independent component, thresholding and Bayesian estimation (2003) Magnetic Resonance in Medicine, 49, pp. 885-894; Chou, Y.C., Teng, M.M.H., Guo, W.Y., Hsieh, J.C., Wu, Y.T., Classification of Hemodynamics from Dynamic-susceptibility-contrast Magnetic Resonance (DSC-MR) Brain Images Using Noiseless Independent Factor Analysis (2007) Medical Image Analysis, 11, pp. 242-253; Wu, Y.T., Chou, Y.C., Guo, W.Y., Yeh, T.C., Hsieh, J.C., Classification of Spatiotemporal Hemodynamics From Brain Perfusion MR Images Using Expectation-Maximization Estimation With Finite Mixture of Multivariate Gaussian Distributions (2007) Magnetic Resonance in Medicine, 57, pp. 181-191; Hyvarinen, A., Karhunen, J., Oja, E., (2001) Independent Component Analysis, , New York: John Wiley & Sons, Inc; Bishop, C.M., (1995) Neural networks for pattern recognition, , Oxford: Oxford University Press; Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via the EM algorithm (1977) Journal of the Royal Statistical Society, 39, pp. 1-38; Calamante, F., Gadian, D.G., Connelly, A., Delay and dispersion effects in dynamic susceptibility contrast MRI: Simulations using singular value decomposition (2000) Magnetic Resonance in Medicine, 44, pp. 466-473; Calamante, F., Willats, L., Gadian, D.G., Connelly, A., Bolus delay and dispersion in perfusion MRI: Implications for tissue predictor models in stroke (2006) Magnetic Resonance in Medicine, 55, pp. 1180-1185; Willats, L., Connelly, A., Calamante, F., Improved Deconvolution of Perfusion MRI Data in the Presence of Bolus Delay and Dispersion (2006) Magnetic Resonance in Medicine, 56, pp. 146-156; Otsu, N., A threshold selection method from gray-level histograms (1979) IEEE Transactions on System, Man, And Cybernetics, SMC-9, pp. 62-66; Gonzalez, R.C., Woods, R.E., (2002) Digital Image Processing, , New Jersey: Prentice-Hall, Inc; Wishart, D., An Algorithm for Hierarchical Classifications (1969) Biometrics, 25, pp. 165-170; Ostergaard, L., Weisskoff, R.M., Chesler, D.A., Gyldensted, C., Rosen, B., High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis (1996) Magnetic Resonance in Medicine, 36, pp. 715-725; Rempp, K.A., Brix, G., Wenz, F., Becker, C.R., Guckel, F., Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging (1994) Radiology, 193, pp. 637-641; Zierler, K.L., Theoretical basis of indicator-dilution methods for measuring flow and volume (1962) Circulation Research, 10, pp. 393-407; Wu, O., Ostergaard, L., Weisskoff, R.M., Benner, T., Rosen, B.R., Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix (2003) Magnetic Resonance in Medicine, 50, pp. 164-174; Schwarz, G., Estimating the Dimension of a Model (1978) Annals of Statistics, 6, pp. 461-464; Calamante, F., Thomas, D.L., Pell, G.S., Wiersma, J., Turner, R., Measuring cerebral blood flow using magnetic resonance imaging techniques (1999) Journal of Cerebral Blood Flow and Metabolism, 19, pp. 701-735; Wenz, F., Rempp, K., Brix, G., Knopp, M.V., Guckel, F., Age dependency of the regional cerebral blood volume (rCBV) measured with dynamic susceptibility contrast MR imaging (DSC) (1996) Magnetic Resonance Imaging, 14, pp. 157-162; Butcher, K., Parsons, M., Allport, L., Lee, S.B., Barber, P.A., Rapid Assessment of Perfusion-Diffusion Mismatch (2008) Stroke, 39, pp. 75-81; Kane, I., Sandercock, P., Wardlaw, J., Magnetic resonance perfusion diffusion mismatch and thrombolysis in acute ischaemic stroke: a systematic review of the evidence to date (2007) J Neurol Neurosurg Psychiatry, 78, pp. 485-491; Von Neumann, J., Morgenstern, O., (1980), Theory of games and economic behavior: Princeton University PressNash Jr., J.F., The bargaining problem (1950) Econometrica: Journal of the Econometric Society, pp. 155-162; Nash, J., Non-cooperative games (1951) The Annals of Mathematics, 54, pp. 286-295; Myerson, R.B., Graphs and cooperation in games (1977) Mathematics of Operations Research, 2, pp. 225-229; Wong, K.K.L., A geometrical perspective for the bargaining problem (2010) PloS One, 5, pp. e10331; Wong, K.K.L., Chawla, S., An Efficient Approach to Detect Cluster Locations using Cross-Plots (2003) 2nd Workshop in conjunction with 9th ACM SIGKDD International Conference on Knowledge Discovery, , Data Mining Washington DC, USA; Wong, K.K.L., Abbott, D., Automatic target recognition based on cross-plot (2011) PloS One, 6, pp. e25621; Wong, K.K., Kelso, R.M., Worthley, S.G., Sanders, P., Mazumdar, J., Medical imaging and processing methods for cardiac flow reconstruction (2009) Journal of Mechanics in Medicine and Biology, 9, pp. 1-20

PY - 2013

Y1 - 2013

N2 - 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. © 2013 Lu et al.

AB - 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. © 2013 Lu et al.

KW - adult

KW - aged

KW - algorithm

KW - article

KW - brain blood flow

KW - brain infarction

KW - brain perfusion

KW - brain tissue

KW - case report

KW - cluster analysis

KW - controlled study

KW - dispersion

KW - expectation maximization algorithm

KW - female

KW - hemodynamics

KW - human

KW - internal carotid artery occlusion

KW - kernel method

KW - male

KW - Monte Carlo method

KW - moyamoya disease

KW - tissue section

KW - Adolescent

KW - Adult

KW - Aged

KW - Algorithms

KW - Brain

KW - Carotid Artery, Internal

KW - Carotid Stenosis

KW - Cerebrovascular Circulation

KW - Computer Simulation

KW - Female

KW - Hemodynamics

KW - Humans

KW - Magnetic Resonance Imaging

KW - Male

KW - Middle Aged

KW - Monte Carlo Method

KW - Moyamoya Disease

KW - Perfusion Imaging

KW - Young Adult

UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84880438188&partnerID=40&md5=2bcf15e7980ce0825d644c1535fb7278

UR - https://www.scopus.com/results/citedbyresults.uri?sort=plf-f&cite=2-s2.0-84880438188&src=s&imp=t&sid=04827fbb24493a50c4865aec62f457d9&sot=cite&sdt=a&sl=0&origin=recordpage&editSaveSearch=&txGid=cf205e5fc35f8320b37323c75110f6f6

U2 - 10.1371/journal.pone.0068986

DO - 10.1371/journal.pone.0068986

M3 - Article

SN - 1932-6203

VL - 8

JO - PLoS One

JF - PLoS One

IS - 7

ER -