Estimation of kinetic parameters without input functions: Analysis of three methods for multichannel blind identification

IEEE Transactions on Biomedical Engineering

Volumn 49, Issue 11, 2002, Pages 1318-1327

  Riabkov, Dmitri Y ^a   Di Bella, Edward V R ^b  

^a UNIVERSITY OF UTAH   (United States)

^b UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

Author keywords

Blind identification; Blood input; IQML; Perfusion; Physiological compartment model; Tissue response

Indexed keywords

ALGORITHMS; BLOOD; CONVOLUTION; MEDICAL IMAGING; STATISTICAL METHODS; TISSUE;

BLOOD STREAMS;

BIOMEDICAL ENGINEERING;

ACCURACY; ARTICLE; CALCULATION; COMPARTMENT MODEL; DIAGNOSTIC IMAGING; KINETICS; MATHEMATICAL ANALYSIS; MAXIMUM LIKELIHOOD METHOD; MULTICHANNEL RECORDER; STATISTICAL ANALYSIS; TISSUE REACTION;

ALGORITHMS; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE PROCESSING, COMPUTER-ASSISTED; LIKELIHOOD FUNCTIONS; MAGNETIC RESONANCE IMAGING; MODELS, BIOLOGICAL; QUALITY CONTROL; RADIOACTIVE TRACERS; RADIOPHARMACEUTICALS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; STOCHASTIC PROCESSES; TISSUE DISTRIBUTION; TOMOGRAPHY, EMISSION-COMPUTED;

EID: 0036845255     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2002.804588     Document Type: Article

References (20)

1. J.-P. Vallee, H. D. Sostman, J. R. MacFall, T. Wheeler, L. W. Hedlund, C. E. Spritzer, and R. E. Coleman, "MRI quantitative myocardial perfusion with compartmental analysis: A rest and stress study," Magn. Reson. Med, vol. 38, pp. 981-989, 1997.
2. E. V. R. Di Bella, R. Clackdoyle, and G. T. Gullberg, "Blind estimation of compartmental model parameters," Phys. Med. Biol. vol. 44, pp. 765-780, 1999.
3. 18 F-Fluoro-2-Deoxyglucose, the Patlak method, and an image derived input function," J. Cereb. Blood Flow Metab., vol. 18, no. 7, pp. 716-723, 1998.
4. S. Takikawa, V. Dhawan, P. Spetsieres, W. Robeson, T. Chaly, R. Dahl, D. Margouleff, and D. Eidelberg, "Noninvasive quantitative fluorodeoxyglucose PET studies with an estimated input function derived from a population-based arterial blood curve," Radiology, vol. 188, no. 1, pp. 131-136, 1993.
5. M. I. Gürelli and C. L. Nikias, "EVAM: An eigenvector-based algorithm for multichannel blind deconvolution of input colored signals," IEEE Trans. Signal Processing, vol. 43, pp. 134-149, Jan. 1995.
6. C.-H. Lau, P.-K. D. Lun, and D. Feng, "Non-invasive quantification of physiological processes with dynamic PET using blind deconvolution," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, vol. 3, Seattle, WA, 1998, pp. 1805-1808.
7. 15O] water and PET. Determination without measured input function," in Cerebral Blood Flow Imaging Conf. Bethesda, MD; Academic, 1996, pp. 185-195.
8. K.-P. Wong, D. Feng, S. R. Meikle, and M. J. Fulham, "Simultaneous estimation of physiological parameters and the input function - In vivo PET data," IEEE Trans. Inform. Technol. Biomed., vol. 5, pp. 67-76, Mar. 2001.
9. D. Feng, K.-P. Wong, C.-M. Wu, and W. C. Siu, "A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: Theory and simulation study," IEEE Trans. Inform. Technol. Biomed., vol. 1, pp. 243-254, Dec. 1997.
10. G. H. Golub and V. Pereyra, "The differentiation of pseudo-inverses and nonlinear least squares problems whose variables separate," SIAM J. Numer. Anal., vol. 10, no. 2, pp. 413-432, April 1973.
11. L. Tong and S. Perreau, "Multichannel blind identification: From subspace to maximum likelihood methods," Proc. IEEE, vol. 86, no. 10, pp. 1951-1968, Oct. 1998.
12. Y. Bresler and A. Macovski, "Exact maximum likelihood parameter estimation of superimposed exponential signals in noise," IEEE Trans. Acoust., Speech Signal Processing, vol. ASSP-34, pp. 1081-1089, Oct. 1986.
13. R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, "A limited memory algorithm for bound constrained optimization," SIAM J. Scientific Computing, vol. 16, no. 5, pp. 1190-1208, 1995.
14. W. H. Press, S. F. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C: the Art of Scientific Computing, 2nd Ed. Cambridge, U.K.: Cambridge University Press, 1992.
15. Matlab Optimization Toolbox User's Guide Version 5, The MathWorks, Natick, MA, 1997.
16. K. M. Donahue, D. Burstein, M. L. Gray, and W. J. Manning, "Studies of Gd-DTPA relaxivity and proton exchange rates in tissue," Magn. Reson. Med., pp. 66-76, 1994.
17. N. Wilke, M. Jerosch-Herold, Y. Wang, Y. Huang, B. V. Christensen, A. E. Stilllman, K. Ugurbil, K. McDonald, and R. F. Wilson, "Myocardial perfusion reserve: Assessment with multisection, quantitative, first-pass MR imaging," Radiology, vol. 204, pp. 373-384, 1997.
18. J.-P. Vallee, F. Lazeyras, L. Kasuboski, P. Chatelain, N. Howarth, A. Righetti, and D. Didier, "Quantification of myocardial perfusion with FAST sequence and GdBolus in patients with normal cardiac function," J. Magn. Reson. Imag., vol. 9, pp. 197-203, 1999.
19. J.-P. Vallee, H. D. Sostman, J. R. MacFall, T. R. DeGrado, J. Zhang, L. Sebbag, F. R. Cobb, T. Wheeler, L. W. Hedlund, T. G. Turkington, C. E. Spritzer, and R. E. Coleman, "Quantification of myocardial perfusion by MRI after coronary occlusion," Magn. Reson. Med., vol. 40, pp. 287-297, 1998.
20. N. Al-Saadi, E. Nagel, M. Gross, A. Bornstedt, B. Schnackenburg, C. Klein, W. Klimek, H. Oswald, and E. Fleck, "Noninvasive detection of myocardial ischemia from perfusion reserve based on cardiovascular magnetic resonance," Circulation, vol. 101, pp. 1379-1383, 2000.