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Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging


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[1] Senda M, Nishizawa S, Yonekura Y, et al. Measurement of arterial time-activity curve by monitoring continuously drawn arterial blood with an external detector: errors and corrections. Ann Nucl Med. 1988;2(1):7-12.10.1007/BF031645803275106Search in Google Scholar

[2] Boellaard R, Van Lingen A, Van Balen SC, et al. Characteristics of a new fully programmable blood sampling device for monitoring blood radioactivity during PET. Eur J Nucl Med. 2001; 28(1):81-89.10.1007/s00259000040511202456Search in Google Scholar

[3] Convert L, Morin-Brassard G, Cadorette J, et al. A new tool for molecular imaging: the microvolumetric Beta blood counter. J Nucl Med. 2007;48(7):1197-1206.10.2967/jnumed.107.04260617574990Search in Google Scholar

[4] Ranicar AS, Williams CW, Schnorr L, et al. The on-line monitoring of continuously withdrawn arterial blood during PET studies using a single BGO/photomultiplier assembly and non-stick tubing. Med Prog Technol. 1991;17(3-4):259-264.Search in Google Scholar

[5] Lin KP, Huang SC, Choi Y, Brunken, et al. Correction of spillover radioactivities for estimation of the blood time-activity curve from the imaged LV chamber in cardiac dynamic FDG PET studies. Phys Med Biol. 1995;40(4):629-642.10.1088/0031-9155/40/4/0097610118Search in Google Scholar

[6] Mourik JE, Lubberink M, Schuitemaker A, et al. Image-derived input functions for PET brain studies. Eur J Nucl Med Mol Imaging. 2009;36(3): 463-471.10.1007/s00259-008-0986-819030855Search in Google Scholar

[7] Houston AS. The effect of apex-finding errors on factor images obtained from factor analysis and oblique transformation. Phys Med Biol. 1984;29(9):1109-1116.10.1088/0031-9155/29/9/0076483975Search in Google Scholar

[8] Wu HM, Hoh CK, Choi Y, et al. Factor analysis for extraction of blood time-activity curves in dynamic FDG-PET studies. J Nucl Med. 1995;36(9):1714-1722.Search in Google Scholar

[9] Bentourkia M. Kinetic modeling of PET-FDG in the brain without blood sampling. Comput Med Imaging Graph. 2006;30(8):447-451.10.1016/j.compmedimag.2006.07.00216978837Search in Google Scholar

[10] Buvat I, Benali H, Frouin F, et al. Target apex-seeking in factor analysis of medical image sequences. Phys Med Biol. 1993;38(1):123-138.10.1088/0031-9155/38/1/0098426863Search in Google Scholar

[11] Lee JS, Lee DS, Ahn JY, et al. Blind separation of cardiac components and extraction of input function from H215O dynamic myocardial PET using independent component analysis. J Nucl Med. 2001;42(6):938-943.Search in Google Scholar

[12] Naganawa M, Kimura Y, Ishii K, et al. Extraction of a plasma time-activity curve from dynamic brain PET images based on independent component analysis. IEEE Trans Biomed Eng. 2005;52(2):201-209.10.1109/TBME.2004.84019315709657Search in Google Scholar

[13] Naganawa M, Kimura Y, Ishii K, et al. Temporal and spatial blood information estimation using Bayesian ICA in dynamic cerebral positron emission tomography. Dig Sig Process. 2007; 17(5):979-993.10.1016/j.dsp.2007.03.002Search in Google Scholar

[14] Chen K, Chen X, Renaut R, et al. Characterization of the image-derived carotid artery input function using independent component analysis for the quantitation of [18F] fluorodeoxyglucose positron emission tomography images. Phys Med Biol. 2007;52(23):7055-7071.10.1088/0031-9155/52/23/019Search in Google Scholar

[15] Su KH, Lee JS, Yang YW, et al. Partial volume correction of the microPET blood input function using ensemble learning independent component analysis. Phys Med Biol. 2009;54(6):1823-1846.10.1088/0031-9155/54/6/026Search in Google Scholar

[16] Margadan-Mendez M, Juslin A, Nesterov SV, et al. ICA Based Automatic Segmentation of Dynamic H215O Cardiac PET Images. IEEE Trans Info Tech Biomed. 2010;14(3):795-802.10.1109/TITB.2007.910744Search in Google Scholar

[17] Fu Z, Tantawy MN, Peterson TE. Ensemble learning (EL) independent component analysis (ICA) approach to derive blood input function from FDG-PET images in small animal. IEEE Nucl Sci Symp Conf Rec. 2006;5:2708-2712.10.1109/NSSMIC.2006.356439Search in Google Scholar

[18] Mabrouk R, Dubeau F, Bentabet L. Dynamic cardiac PET imaging: extraction of time-activity curves using ICA and a generalized Gaussian distribution model. IEEE Trans Biomed Eng. 2012;60(1):63-71.10.1109/TBME.2012.2221463Search in Google Scholar

[19] Moussaoui S, Brie D, Mohammad-Djafari A, et al. Separation of non-negative mixture of non-negative sources using a Bayesian approach and MCMC sampling. IEEE Trans Sig Process. 2006;54(11):4133-4145.10.1109/TSP.2006.880310Search in Google Scholar

[20] Berradja K, Boughanmi N. Assessment of brain glucose metabolism with input function determined from Brain PET images by means of Bayesian ICA and MCMC methods. Comput Med Imaging Graph. 2012;36(8):620-626.10.1016/j.compmedimag.2012.07.002Search in Google Scholar

[21] Robert C. Monte Carlo Statistical Methods. Berlin: Springer-Verlag; 1999.10.1007/978-1-4757-3071-5Search in Google Scholar

[22] Fitzgerald W. Markov chain Monte Carlo methods with applications to signal processing. Sig Process. 2001;81(1):3-18.10.1016/S0165-1684(00)00187-0Search in Google Scholar

[23] Bergeron M, Cadorette J, Beaudoin J-F, et al. Evaluation of the Performance-Based Digital LabPET APD PET Scanner. IEEE Trans Nucl Sci. 2009;56(1).10.1109/TNS.2008.2010257Search in Google Scholar

[24] Phelps ME, Huang SC, Hoffman EJ, et al. Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-D-glucose: validation of method. Ann Neurol. 1979;6(5):371-388.10.1002/ana.410060502117743Search in Google Scholar

eISSN:
1898-0309
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Medicine, Biomedical Engineering, Physics, Technical and Applied Physics, Medical Physics