Blind source separation of hemodynamics from magnetic resonance perfusion brain images using independent factor analysis

Yu-Te Wu; Yen-Chun Chou; Chia-Feng Lu; Wan-Yuo Guo

doi:10.1155/2010/360568

Blind source separation of hemodynamics from magnetic resonance perfusion brain images using independent factor analysis

Yu-Te Wu, Yen-Chun Chou, Chia-Feng Lu, Wan-Yuo Guo

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Perfusion magnetic resonance brain imaging induces temporal signal changes on brain tissues, manifesting distinct blood-supply patterns for the profound analysis of cerebral hemodynamics. We employed independent factor analysis to blindly separate such dynamic images into different maps, that is, artery, gray matter, white matter, vein and sinus, and choroid plexus, in conjunction with corresponding signal-time curves. The averaged signal-time curve on the segmented arterial area was further used to calculate the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT). The averaged ratios for rCBV, rCBF, and MTT between gray and white matters for normal subjects were congruent with those in the literature. Copyright © 2010 Yen-Chun Chou et al.

Original language	English
Journal	International Journal of Biomedical Imaging
Volume	2010
DOIs	https://doi.org/10.1155/2010/360568
Publication status	Published - 2010
Externally published	Yes

Keywords

Brain images
Brain imaging
Brain tissue
Cerebral blood flow
Cerebral blood volume
Cerebral hemodynamics
Dynamic images
Gray matter
Independent factor analysis
Magnetic resonance perfusions
Mean transit time
Temporal signals
Time curves
White matter
Blood
Brain
Hemodynamics
Hydrodynamics
Inductively coupled plasma
Magnetic resonance
Blind source separation

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10.1155/2010/360568

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@article{547068ff7f6a4980b078c7fc9c07f96b,

title = "Blind source separation of hemodynamics from magnetic resonance perfusion brain images using independent factor analysis",

abstract = "Perfusion magnetic resonance brain imaging induces temporal signal changes on brain tissues, manifesting distinct blood-supply patterns for the profound analysis of cerebral hemodynamics. We employed independent factor analysis to blindly separate such dynamic images into different maps, that is, artery, gray matter, white matter, vein and sinus, and choroid plexus, in conjunction with corresponding signal-time curves. The averaged signal-time curve on the segmented arterial area was further used to calculate the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT). The averaged ratios for rCBV, rCBF, and MTT between gray and white matters for normal subjects were congruent with those in the literature. Copyright {\textcopyright} 2010 Yen-Chun Chou et al.",

keywords = "Brain images, Brain imaging, Brain tissue, Cerebral blood flow, Cerebral blood volume, Cerebral hemodynamics, Dynamic images, Gray matter, Independent factor analysis, Magnetic resonance perfusions, Mean transit time, Temporal signals, Time curves, White matter, Blood, Brain, Hemodynamics, Hydrodynamics, Inductively coupled plasma, Magnetic resonance, Blind source separation",

author = "Yu-Te Wu and Yen-Chun Chou and Chia-Feng Lu and Wan-Yuo Guo",

note = "被引用次數：1 Export Date: 31 March 2016 通訊地址: Wu, Y.-T.; Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Li-Nong Street, Pei-Tou Taipei 112, Taiwan; 電子郵件： [email protected] 參考文獻: Stergaard, L., Weisskoff, R.M., Chesler, D.A., Gyldensted, G., Rosen, B.R., 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 (5), pp. 715-725; Stergaard, L., Sorensen, A.G., Kwong, K.K., Weisskoff, R.M., Gyldensted, C., Rosen, B.R., 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 (5), pp. 726-736; 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 (7), pp. 701-735; Rempp, K.A., Brix, G., Wenz, F., Becker, C.R., Guckel, F., Lorenz, W.J., Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging (1994) Radiology, 193 (3), pp. 637-641; Rosen, B.R., Belliveau, J.W., Vevea, J.M., Brady, T.J., Perfusion imaging with NMR contrast agents (1990) Magnetic Resonance in Medicine, 14 (2), pp. 249-265; Schreiber, W.G., Gckel, F., Stritzke, P., Schmiedek, P., Schwartz, A., Brix, G., Cerebral blood flow and cerebrovascular reserve capacity: Estimation by dynamic magnetic resonance imaging (1998) Journal of Cerebral Blood Flow and Metabolism, 18 (10), pp. 1143-1156; Sorensen, A.G., Tievsky, A.L., Stergaard, L., Weisskoff, R.M., Rosen, B.R., Contrast agents in functional MR imaging (1997) Journal of Magnetic Resonance Imaging, 7 (1), pp. 47-55; Van Osch, M.J.P., Vonken, E.J., Wu, O., Viergever, M.A., Van Der Grond, J., Bakker, C.J., Model of the human vasculature for studying the influence of contrast injection speed on cerebral perfusion MRI (2003) Magnetic Resonance in Medicine, 50 (3), pp. 614-622; Wenz, F., Rempp, K., Brix, G., Age dependency of the regional cerebral blood volume (rCBV) measured with dynamic susceptibility contrast MR imaging (DSC) (1996) Magnetic Resonance Imaging, 14 (2), pp. 157-162; Wu, O., Stergaard, L., Weisskoff, R.M., Benner, T., Rosen, B.R., Sorensen, A.G., 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 (1), pp. 164-174; Lassen, N.A., Perl, W., (1979) Tracer Kinetic Methods in Medical Physiology, , New York, NY, USA Raven; Zierler, K.L., Theoretical basis of indicator-dilution methods for measuring flow and volume (1962) Circulation Research, 10 (3), pp. 393-407; Aronen, H.J., Glass, J., Pardo, F.S., Echo-planar MR cerebral blood volume mapping of gliomas. Clinical utility (1995) Acta Radiologica, 36 (5), pp. 520-528; Sorensen, A.G., Copen, W.A., Stergaard, L., Hyperacute stroke: Simultaneous measurement of relative cerebral blood volume, relative cerebral blood flow, and mean tissue transit time (1999) Radiology, 210 (2), pp. 519-527; Ernst, T.M., Chang, L., Witt, M.D., Cerebral toxoplasmosis and lymphoma in aids: Perfusion MR imaging experience in 13 patients (1998) Radiology, 208 (3), pp. 663-669; Yamada, I., Himeno, Y., Nagaoka, T., Moyamoya disease: Evaluation with diffusion-weighted and perfusion echo-planar MR imaging (1999) Radiology, 212 (2), pp. 340-347; Ohashi, K., Fernandez-Ulloa, M., Hall, L.C., SPECT, magnetic resonance and angiographic features in a moyamoya patient before and after external-to-internal carotid artery bypass (1992) Journal of Nuclear Medicine, 33 (9), pp. 1692-1695; Wiart, M., Rognin, N., Berthezene, Y., Nighoghossian, N., Froment, J.C., Baskurt, A., Perfusion-based segmentation of the human brain using similarity mapping (2001) Magnetic Resonance in Medicine, 45 (2), pp. 261-268; Martel, A.L., Moody, A.R., Allder, S.J., Delay, G.S., Morgan, P.S., Extracting parametric images from dynamic contrast-enhanced MRI studies of the brain using factor analysis (2001) Medical Image Analysis, 5 (1), pp. 29-39; Ahn, J.Y., Lee, D.S., Lee, J.S., Quantification of regional myocardial blood flow using dynamic H 2 O 15 PET and factor analysis (2001) Journal of Nuclear Medicine, 42 (5), pp. 782-787; Hermansen, F., Ashburner, J., Spinks, T.J., Kooner, J.S., Camici, P.G., Lammertsma, A.A., Generation of myocardial factor images directly from the dynamic oxygen-15-water scan without use of an oxygen-15-carbon monoxide blood-pool scan (1998) Journal of Nuclear Medicine, 39 (10), pp. 1696-1702; Wu, H.M., Hoh, C.K., Choi, Y., Factor analysis for extraction of blood time-activity curves in dynamic FDG-PET studies (1995) Journal of Nuclear Medicine, 36 (9), pp. 1714-1722; Barber, D.C., The use of principal components in the quantitative analysis of gamma camera dynamic studies (1980) Physics in Medicine and Biology, 25 (2), pp. 283-292; Di Paola, R., Bazin, J.P., Aubry, F., Handling of dynamic sequences in nuclear medicine (1982) IEEE Transactions on Nuclear Science, 29, pp. 1310-1321; Houston, A.S., The effect of apex-finding errors on factor images obtained from factor analysis and oblique transformation (nuclear medicine) (1984) Physics in Medicine and Biology, 29 (9), pp. 1109-1116; 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 (1), pp. 181-191; Attias, H., Independent factor analysis (1999) Neural Computation, 11 (4), pp. 803-851; Nagarajan, S.S., Attias, H.T., Ii, E.H.K., Sekihara, K., A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity (2006) NeuroImage, 30 (2), pp. 400-416; Otsu, N., A threshold selection method from gray-level histograms (1979) IEEE Transactions on Systems, Man, and Cybernetics, 9 (1), pp. 62-66; Weisskoff, R.M., Zuo, C.S., Boxerman, J.L., Rosen, B.R., Microscopic susceptibility variation and transverse relaxation: Theory and experiment (1994) Magnetic Resonance in Medicine, 31 (6), pp. 601-610; Kety, S.S., Blood-tissue exchange methods: Theory of blood-tissue exchange and its application to measurement of blood flow (1960) Methods in Medical Research, 8, pp. 223-227; Friedman, J.H., Exploratory projection pursuit (1987) Journal of the American Statistical Association, 82 (397), pp. 249-266; Fyfe, C., A comparative study of two neural methods of exploratory projection pursuit (1997) Neural Networks, 10 (2), pp. 257-262; MacKay, D.J.C., Bayesian interpolation (1992) Neural Computation, 4 (3), pp. 415-447",

year = "2010",

doi = "10.1155/2010/360568",

language = "English",

volume = "2010",

journal = "International Journal of Biomedical Imaging",

issn = "1687-4188",

publisher = "Hindawi Publishing Corporation",

}

TY - JOUR

T1 - Blind source separation of hemodynamics from magnetic resonance perfusion brain images using independent factor analysis

AU - Wu, Yu-Te

AU - Chou, Yen-Chun

AU - Lu, Chia-Feng

AU - Guo, Wan-Yuo

N1 - 被引用次數：1 Export Date: 31 March 2016 通訊地址: Wu, Y.-T.; Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Li-Nong Street, Pei-Tou Taipei 112, Taiwan; 電子郵件： [email protected] 參考文獻: Stergaard, L., Weisskoff, R.M., Chesler, D.A., Gyldensted, G., Rosen, B.R., 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 (5), pp. 715-725; Stergaard, L., Sorensen, A.G., Kwong, K.K., Weisskoff, R.M., Gyldensted, C., Rosen, B.R., 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 (5), pp. 726-736; 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 (7), pp. 701-735; Rempp, K.A., Brix, G., Wenz, F., Becker, C.R., Guckel, F., Lorenz, W.J., Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging (1994) Radiology, 193 (3), pp. 637-641; Rosen, B.R., Belliveau, J.W., Vevea, J.M., Brady, T.J., Perfusion imaging with NMR contrast agents (1990) Magnetic Resonance in Medicine, 14 (2), pp. 249-265; Schreiber, W.G., Gckel, F., Stritzke, P., Schmiedek, P., Schwartz, A., Brix, G., Cerebral blood flow and cerebrovascular reserve capacity: Estimation by dynamic magnetic resonance imaging (1998) Journal of Cerebral Blood Flow and Metabolism, 18 (10), pp. 1143-1156; Sorensen, A.G., Tievsky, A.L., Stergaard, L., Weisskoff, R.M., Rosen, B.R., Contrast agents in functional MR imaging (1997) Journal of Magnetic Resonance Imaging, 7 (1), pp. 47-55; Van Osch, M.J.P., Vonken, E.J., Wu, O., Viergever, M.A., Van Der Grond, J., Bakker, C.J., Model of the human vasculature for studying the influence of contrast injection speed on cerebral perfusion MRI (2003) Magnetic Resonance in Medicine, 50 (3), pp. 614-622; Wenz, F., Rempp, K., Brix, G., Age dependency of the regional cerebral blood volume (rCBV) measured with dynamic susceptibility contrast MR imaging (DSC) (1996) Magnetic Resonance Imaging, 14 (2), pp. 157-162; Wu, O., Stergaard, L., Weisskoff, R.M., Benner, T., Rosen, B.R., Sorensen, A.G., 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 (1), pp. 164-174; Lassen, N.A., Perl, W., (1979) Tracer Kinetic Methods in Medical Physiology, , New York, NY, USA Raven; Zierler, K.L., Theoretical basis of indicator-dilution methods for measuring flow and volume (1962) Circulation Research, 10 (3), pp. 393-407; Aronen, H.J., Glass, J., Pardo, F.S., Echo-planar MR cerebral blood volume mapping of gliomas. Clinical utility (1995) Acta Radiologica, 36 (5), pp. 520-528; Sorensen, A.G., Copen, W.A., Stergaard, L., Hyperacute stroke: Simultaneous measurement of relative cerebral blood volume, relative cerebral blood flow, and mean tissue transit time (1999) Radiology, 210 (2), pp. 519-527; Ernst, T.M., Chang, L., Witt, M.D., Cerebral toxoplasmosis and lymphoma in aids: Perfusion MR imaging experience in 13 patients (1998) Radiology, 208 (3), pp. 663-669; Yamada, I., Himeno, Y., Nagaoka, T., Moyamoya disease: Evaluation with diffusion-weighted and perfusion echo-planar MR imaging (1999) Radiology, 212 (2), pp. 340-347; Ohashi, K., Fernandez-Ulloa, M., Hall, L.C., SPECT, magnetic resonance and angiographic features in a moyamoya patient before and after external-to-internal carotid artery bypass (1992) Journal of Nuclear Medicine, 33 (9), pp. 1692-1695; Wiart, M., Rognin, N., Berthezene, Y., Nighoghossian, N., Froment, J.C., Baskurt, A., Perfusion-based segmentation of the human brain using similarity mapping (2001) Magnetic Resonance in Medicine, 45 (2), pp. 261-268; Martel, A.L., Moody, A.R., Allder, S.J., Delay, G.S., Morgan, P.S., Extracting parametric images from dynamic contrast-enhanced MRI studies of the brain using factor analysis (2001) Medical Image Analysis, 5 (1), pp. 29-39; Ahn, J.Y., Lee, D.S., Lee, J.S., Quantification of regional myocardial blood flow using dynamic H 2 O 15 PET and factor analysis (2001) Journal of Nuclear Medicine, 42 (5), pp. 782-787; Hermansen, F., Ashburner, J., Spinks, T.J., Kooner, J.S., Camici, P.G., Lammertsma, A.A., Generation of myocardial factor images directly from the dynamic oxygen-15-water scan without use of an oxygen-15-carbon monoxide blood-pool scan (1998) Journal of Nuclear Medicine, 39 (10), pp. 1696-1702; Wu, H.M., Hoh, C.K., Choi, Y., Factor analysis for extraction of blood time-activity curves in dynamic FDG-PET studies (1995) Journal of Nuclear Medicine, 36 (9), pp. 1714-1722; Barber, D.C., The use of principal components in the quantitative analysis of gamma camera dynamic studies (1980) Physics in Medicine and Biology, 25 (2), pp. 283-292; Di Paola, R., Bazin, J.P., Aubry, F., Handling of dynamic sequences in nuclear medicine (1982) IEEE Transactions on Nuclear Science, 29, pp. 1310-1321; Houston, A.S., The effect of apex-finding errors on factor images obtained from factor analysis and oblique transformation (nuclear medicine) (1984) Physics in Medicine and Biology, 29 (9), pp. 1109-1116; 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 (1), pp. 181-191; Attias, H., Independent factor analysis (1999) Neural Computation, 11 (4), pp. 803-851; Nagarajan, S.S., Attias, H.T., Ii, E.H.K., Sekihara, K., A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity (2006) NeuroImage, 30 (2), pp. 400-416; Otsu, N., A threshold selection method from gray-level histograms (1979) IEEE Transactions on Systems, Man, and Cybernetics, 9 (1), pp. 62-66; Weisskoff, R.M., Zuo, C.S., Boxerman, J.L., Rosen, B.R., Microscopic susceptibility variation and transverse relaxation: Theory and experiment (1994) Magnetic Resonance in Medicine, 31 (6), pp. 601-610; Kety, S.S., Blood-tissue exchange methods: Theory of blood-tissue exchange and its application to measurement of blood flow (1960) Methods in Medical Research, 8, pp. 223-227; Friedman, J.H., Exploratory projection pursuit (1987) Journal of the American Statistical Association, 82 (397), pp. 249-266; Fyfe, C., A comparative study of two neural methods of exploratory projection pursuit (1997) Neural Networks, 10 (2), pp. 257-262; MacKay, D.J.C., Bayesian interpolation (1992) Neural Computation, 4 (3), pp. 415-447

PY - 2010

Y1 - 2010

N2 - Perfusion magnetic resonance brain imaging induces temporal signal changes on brain tissues, manifesting distinct blood-supply patterns for the profound analysis of cerebral hemodynamics. We employed independent factor analysis to blindly separate such dynamic images into different maps, that is, artery, gray matter, white matter, vein and sinus, and choroid plexus, in conjunction with corresponding signal-time curves. The averaged signal-time curve on the segmented arterial area was further used to calculate the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT). The averaged ratios for rCBV, rCBF, and MTT between gray and white matters for normal subjects were congruent with those in the literature. Copyright © 2010 Yen-Chun Chou et al.

AB - Perfusion magnetic resonance brain imaging induces temporal signal changes on brain tissues, manifesting distinct blood-supply patterns for the profound analysis of cerebral hemodynamics. We employed independent factor analysis to blindly separate such dynamic images into different maps, that is, artery, gray matter, white matter, vein and sinus, and choroid plexus, in conjunction with corresponding signal-time curves. The averaged signal-time curve on the segmented arterial area was further used to calculate the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT). The averaged ratios for rCBV, rCBF, and MTT between gray and white matters for normal subjects were congruent with those in the literature. Copyright © 2010 Yen-Chun Chou et al.

KW - Brain images

KW - Brain imaging

KW - Brain tissue

KW - Cerebral blood flow

KW - Cerebral blood volume

KW - Cerebral hemodynamics

KW - Dynamic images

KW - Gray matter

KW - Independent factor analysis

KW - Magnetic resonance perfusions

KW - Mean transit time

KW - Temporal signals

KW - Time curves

KW - White matter

KW - Blood

KW - Brain

KW - Hemodynamics

KW - Hydrodynamics

KW - Inductively coupled plasma

KW - Magnetic resonance

KW - Blind source separation

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U2 - 10.1155/2010/360568

DO - 10.1155/2010/360568

M3 - Article

SN - 1687-4188

VL - 2010

JO - International Journal of Biomedical Imaging

JF - International Journal of Biomedical Imaging

ER -

Blind source separation of hemodynamics from magnetic resonance perfusion brain images using independent factor analysis

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