Advertisement

Estimating the input function non-invasively for FDG-PET quantification with multiple linear regression analysis: simulation and verification with in vivo data

  • Yu-Hua Fang
  • Tsair Kao
  • Ren-Shyan Liu
  • Liang-Chih WuEmail author
Original Article

Abstract

A novel statistical method, namely Regression-Estimated Input Function (REIF), is proposed in this study for the purpose of non-invasive estimation of the input function for fluorine-18 2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) quantitative analysis. We collected 44 patients who had undergone a blood sampling procedure during their FDG-PET scans. First, we generated tissue time-activity curves of the grey matter and the whole brain with a segmentation technique for every subject. Summations of different intervals of these two curves were used as a feature vector, which also included the net injection dose. Multiple linear regression analysis was then applied to find the correlation between the input function and the feature vector. After a simulation study with in vivo data, the data of 29 patients were applied to calculate the regression coefficients, which were then used to estimate the input functions of the other 15 subjects. Comparing the estimated input functions with the corresponding real input functions, the averaged error percentages of the area under the curve and the cerebral metabolic rate of glucose (CMRGlc) were 12.13±8.85 and 16.60±9.61, respectively. Regression analysis of the CMRGlc values derived from the real and estimated input functions revealed a high correlation (r=0.91). No significant difference was found between the real CMRGlc and that derived from our regression-estimated input function (Student’s t test, P>0.05). The proposed REIF method demonstrated good abilities for input function and CMRGlc estimation, and represents a reliable replacement for the blood sampling procedures in FDG-PET quantification.

Keywords

FDG Quantitative analysis Non-invasive Input function CMRGlc 

Notes

Acknowledgements

This research was supported in part by Grant NSC91-2213-E-075-002 from the National Science Council of Taiwan, R.O.C. The authors would also like to thank Dr. Toshiki Shiozaki in the Department of Nuclear Medicine and Diagnostic Imaging, Kyoto University, Japan for providing us with the SIF data.

References

  1. 1.
    Sokoloff L, Reivich M, Kennedy C, DesRosiers MH, Patlak CS, Pettigrew KD, Kakurada D, Shinohara M. The [14C] deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure and normal values in the conscious and anesthetized albino rat. J Neurochem 1977; 28:897–916.PubMedGoogle Scholar
  2. 2.
    Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. 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; 6:371–388.PubMedGoogle Scholar
  3. 3.
    Huang SC, Phelps ME, Hoffman EJ, Sideris K, Selin C, Kuhl DE. Noninvasive determination of local cerebral metabolic rate of glucose in man. Am J Physiol 1980; 238:E69–E82.PubMedGoogle Scholar
  4. 4.
    Huang SC, Phelps ME, Hoffman EJ, Sideris K, Selin C, Kuhl DE. Error sensitivity of fluorodeoxyglucose method for measurement of cerebral metabolic rate of glucose. J Cereb Blood Flow Metabol 1981; 1:391–401.Google Scholar
  5. 5.
    Takikawa S, Dhawan V, Spetsieris P, Robeson W, Chaly T, Dahl R, Margouleff D, Eidelberg D. Non-invasive quantitative fluorodeoxyglucose PET studies with an estimated input function derived from a population-based arterial blood curve. Radiology 1993; 188:131–136.PubMedGoogle Scholar
  6. 6.
    Phillips RL, Chen CY, Wong DF, London ED. An improved method to calculate cerebral metabolic rates of glucose using PET. J Nucl Med 1995; 36:1668–1679.PubMedGoogle Scholar
  7. 7.
    Wakita K, Imahori Y, Ido T, Fujii R, Horii H, Shimizu M, Nakajima S, Mineura K, Nakamura T, Kanatsuna T. Simplification for measuring input function of FDG PET: investigation of 1-point blood sampling method. J Nucl Med 2000; 41:1484–1490.PubMedGoogle Scholar
  8. 8.
    Tsuchida T, Sadato N, Yonekura Y, Nakamura S, Takahashi N, Sugimoto K, Waki A, Yamamoto K, Hayashi N, Ishii Y. Noninvasive measurement of cerebral metabolic rate of glucose using standardized input function. J Nucl Med 1999; 40:1441–1445.PubMedGoogle Scholar
  9. 9.
    Shiozaki T, Sadato N, Senda M, Ishii K, Tsuchida T, Yonekura Y, Fukuda H, Konishi J. Noninvasive estimation of FDG input function for quantification of cerebral metabolic rate of glucose: optimization and multicenter evaluation. J Nucl Med 2000; 41:1612–1618.PubMedGoogle Scholar
  10. 10.
    Feng D, Wong KP, Wu CM, Siu WC. 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 1997; 1:243–254.CrossRefGoogle Scholar
  11. 11.
    Wong KP, Feng D, Meikle SR, Fulham MJ. Simultaneous estimation of physiological parameter and the input function—in vivo PET data. IEEE Trans Inform Technol Biomed 2001; 5:67–76.CrossRefGoogle Scholar
  12. 12.
    Chen K, Bandy D, Reiman E, Huang SC, Lawson M, Feng D, Yun LS, Palant A. Noninvasive quantification of the cerebral metabolic rate for glucose using positron emission tomography,18F-fluoro-2-deoxyglucose, the Patlak method, and an image-derived input function. J Cereb Blood Flow Metab 1998; 18:716–723.CrossRefPubMedGoogle Scholar
  13. 13.
    van der Weerdt AP, Klein LJ, Boellaard R, Visser CA, Visser FC, Lammertsma AA. Image-derived input function for determination of MRGlu in cardiac18F-FDG PET scans. J Nucl Med 2001; 42:1622–1629.PubMedGoogle Scholar
  14. 14.
    Lee JS, Lee DS, Ahn JY, Cheon GJ, Kim SK, Yeo JS, Seo K, Park KS, Chung JK, Lee MC. Blind separation of cardiac components and extraction of input function from H2 15O dynamic myocardial PET using independent component analysis. J Nucl Med 2001; 42:938–943.PubMedGoogle Scholar
  15. 15.
    Wu LC, Feng DG, Wang JK, Lin HM, Chou KL, Liu RS, Yeh SH. Quantitative analysis of FDG PET images. Ann Nucl Med 1998; 11:29–33.Google Scholar
  16. 16.
    Kovacevic N, Lobaugh NJ, Bronskill MJ, Levine B, Feinstein A, Black SE. A robust method for extraction and automatic segmentation of brain images. NeuroImage 2002; 17:1087–1100.CrossRefPubMedGoogle Scholar
  17. 17.
    Feng D, Huang SC, Wang X. Models for computer simulation studies of input functions for tracer kinetic modeling with positron emission tomography. Int J Biomed Comput 1993; 32:95–110.PubMedGoogle Scholar
  18. 18.
    Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood to brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab 1983; 3:1–7.PubMedGoogle Scholar
  19. 19.
    Patlak CS, Blasberg RG. Graphical evaluation of blood to brain transfer constants from multiple-time uptake data: generalizations. J Cereb Blood Flow Metab 1985; 5:584–590.PubMedGoogle Scholar
  20. 20.
    Feng D, Ho D, Chen K, Wu LC, Wang JK, Liu RS, Yeh SH. An evaluation of the algorithms for determining local cerebral metabolic rates of glucose using positron emission tomography dynamic data. IEEE Trans Med Imag 1995; 14:697–710.CrossRefGoogle Scholar
  21. 21.
    Meguro K, Yamaguchi S, Itoh M, Fujiwara T, Yamadori A. Striatal dopamine metabolism correlated with frontotemporal glucose utilization in Alzheimer’s disease: a double-tracer PET study. Neurology 1997; 49:941–945.PubMedGoogle Scholar
  22. 22.
    O’Brien TJ, Hicks RJ, Ware R, Binns DS, Murphy M, Cook MJ. The utility of a 3-dimensional, large-field-of-view, sodium iodide crystal-based PET scanner in the presurgical evaluation of partial epilepsy. J Nucl Med 2001; 42:1158–1165.PubMedGoogle Scholar
  23. 23.
    Kitagawa Y, Sano K, Nishizawa S, Nakamura M, Ogasawara T, Sadato N, Yonekura Y. FDG-PET for prediction of tumour aggressiveness and response to intra-arterial chemotherapy and radiotherapy in head and neck cancer. Eur J Nucl Med Mol Imaging 2003; 30:63–71.CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Yu-Hua Fang
    • 1
  • Tsair Kao
    • 1
  • Ren-Shyan Liu
    • 2
  • Liang-Chih Wu
    • 2
    Email author
  1. 1.Institute of Biomedical EngineeringNational Yang-Ming UniversityTaipeiTaiwan
  2. 2.National PET/Cyclotron CenterTaipei Veterans General HospitalTaipeiTaiwan

Personalised recommendations