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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Abstract

In dynamic cardiac PET FDG studies the assessment of myocardial metabolic rate of glucose (MMRG) requires the knowledge of the blood input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. The IF is contaminated by spillover of radioactivity from the adjacent myocardium and this could cause important error in the estimated MMRG. In this study, we show that the IF can be extracted from the images in a rat heart study with 18F-fluorodeoxyglucose (18F-FDG) by means of Independent Component Analysis (ICA) based on Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling method (BICA). Images of the heart from rats were acquired with the Sherbrooke small animal PET scanner. A region of interest (ROI) was drawn around the rat image and decomposed into blood and tissue using BICA. The Statistical study showed that there is a significant difference (p < 0.05) between MMRG obtained with IF extracted by BICA with respect to IF extracted from measured images corrupted with spillover.

[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.

[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.

[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.

[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 The on-line monitoring of continuously withdrawn arterial blood during PET studies using a single BGO/photomultiplier assembly and non-stick tubing.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.

[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.

[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.

[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.

[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.

[9] Bentourkia M. Kinetic modeling of PET-FDG in the brain without blood sampling. Comput Med Imaging Graph. 2006;30(8):447-451.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[21] Robert C. Monte Carlo Statistical Methods. Berlin: Springer-Verlag; 1999.

[22] Fitzgerald W. Markov chain Monte Carlo methods with applications to signal processing. Sig Process. 2001;81(1):3-18.

[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).

[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.

Polish Journal of Medical Physics and Engineering

The Journal of Polish Society of Medical Physics

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CiteScore 2017: 0.19
ICV 2017 = 103.49

SCImago Journal Rank (SJR) 2017: 0.104
Source Normalized Impact per Paper (SNIP) 2017: 0.233

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