Hemodynamic parameter estimation from ocular fluorescein angiograms

  • Ivo H. M. van Stokkum
  • George N. Lambrou
  • Tom J. T. P. van den Berg
Clinical Investigation


• Background: A method is proposed for parameterizing choroidal blood flow from fluorescein angiograms. • Methods: After digitizing and aligning the angiographic sequence, the intensity build-up curves of fluorescence are analysed per pixel (approx. 10 μm in fundo). Two models are compared. A one-compartment model predicts an exponential build-up curve, from which the following parameters are estimated: maximum fluorescence, dye appearance time and local perfusion rate (reciprocal of the time constant of the exponential). To account for the contribution of the systemic circulation to the shape of the build-up curve, a two-compartment model is used which predicts a bi-exponential curve. • Results: Introduction of the second (systemic) compartment resulted in a significant improvement of fit in 37 of 48 patients studied. The rate constants of the systemic compartment found were mainly in the range of 0.30–1.00 s−1. • Conclusion: For the individual patient, the local perfusion rates may vary strongly, with lower perfusion rates possibly being of prognostic value for ocular diseases such as glaucoma or diabetic retinopathy.


Time Constant Glaucoma Fluorescein Diabetic Retinopathy Perfusion Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Blumenthal M, Gitter KA, Best M, Galin MA (1970) Fluorescein angiography during induced ocular hypertension in man. Am J Ophthalmol 69:39–43Google Scholar
  2. 2.
    Bursell S-E, Clermont AC, Shiba T, King GL (1992) Evaluating retinal circulation using video fluorescein angiography in control and diabetic rats. Curr Eye Res 11:287–295Google Scholar
  3. 3.
    Buzney SM, Weiter JJ (1984) Pathogenesis of diabetic retinal angiopathy: proposed mechanisms and current research. Int Opthalmol Clin 24:1–12Google Scholar
  4. 4.
    Cristini G (1951) Common pathological basis of the nervous ocular systems in chronic glaucoma. Br J Ophthalmol 35:11Google Scholar
  5. 5.
    Dollery CT, Henkind P, Kohner EM, Paterson JW (1968) Effect of raised intraocular pressure on the retinal and choroidal circulation. Invest Ophthalmol 7:191–198Google Scholar
  6. 6.
    Foss SD (1969) A method of exponential curve fitting by numerical integration. Biometrics 25:815–821Google Scholar
  7. 7.
    Geijer C, Bill A (1979) Effects of raised intraocular pressure on retinal, prelaminar, laminar, and retrolaminar optic nerve blood flow in monkeys. Invest Ophthalmol Vis Sci 18:1030–1042Google Scholar
  8. 8.
    Godfrey K (1983) Compartmental models and their application. Academic Press, LondonGoogle Scholar
  9. 9.
    Golub GH, LeVeque RJ (1979) Extensions and uses of the variable projection algorithm for solving nonlinear least squares problems. Proc 1979 Army Numerical Analysis and Comp Conf, ARO Report 79 3:1–12Google Scholar
  10. 10.
    Hayreh SS (1978) Structure and blood supply of the optic nerve. In: Heilmann K, Richardson KT (eds) Glaucoma: conceptions of a disease. Thieme, Stuttgart, pp 78–96Google Scholar
  11. 11.
    Hayreh SS (1978) Pathogenesis of optic nerve damage and visual field defects. In: Heilmann K, Richardson KT (eds) Glaucoma: conceptions of a disease. Thieme, Stuttgart, pp 104–137Google Scholar
  12. 12.
    In den Haak MD, Spoelder HJW, Groen FCA (1992) Matching of images by using automatically selected landmarks. In: Dietz JLG (ed) Proceedings of Computer Science in the Netherlands 1992. Stichting Mathematisch Centrum, Amsterdam, pp 27–40Google Scholar
  13. 13.
    Kaufman L (1975) A variable projection method for solving separable nonlinear least squares problems. BIT 15:49–57Google Scholar
  14. 14.
    Klein GJ, Baumgartner RH, Flower RW (1990) An image processing approach to characterizing choroidal blood flow. Invest Ophthalmol Vis Sci 31:629–637Google Scholar
  15. 15.
    Laatikainen L (1971) Fluorescein angiographic studies of the peripapillary and perilimbal regions in simple, capsular and low-tension glaucoma. Acta Ophthalmol 111 [Suppl]: 9–83Google Scholar
  16. 16.
    Lambrou GN, Sindhunata P, Van den Berg TJTP, Geijssen HC, Vyborny P, Greve EL (1989) Ocular pulse measurements in low-tension glaucoma. In: Lambrou GN, Greve EL (eds) Ocular blood flow in glaucoma. Kugler & Ghedini, Amsterdam, pp 115–120Google Scholar
  17. 17.
    Lambrou GN, Van den Berg TJTP, Greve EL (1989) Vascular plerometry of the choroid. An approach to the quantification of choroidal blood flow using computer-assisted processing of fluorescein angiograms. In: Lambrou GN, Greve EL (eds) Ocular blood flow in glaucoma. Kugler & Ghedini, Amsterdam, pp 287–294Google Scholar
  18. 18.
    Lambrou GN, Van den Berg TJTP, Hayreh SS, Greve EL (1991) Automatic estimation of choriocapillaris blood flow parameters by image processing of fast fluorescein angiograms. (ARVO abstracts) Invest Ophthalmol Vis Sci 32 [Suppl]: 866Google Scholar
  19. 19.
    Prünte C, Niesel P (1988) Quantification of choroidal blood-flow parameters using indocyanine green videofluorescence angiography and statistical picture analysis. Graefe's Arch Clin Exp Ophthalmol 226:55–58Google Scholar
  20. 20.
    Seber GAF, Wild CJ (1989) Nonlinear regression. Wiley, New YorkGoogle Scholar
  21. 21.
    Spaeth GL (1977) The pathogenesis of optic nerve damage in glaucoma: contributions of fluorescein angiography. Grune & Stratton, New YorkGoogle Scholar
  22. 22.
    Ulrich WD, Ulrich A, Petzschmann Ä, Ulrich Ch (1988) Okuläre Autoregulation und ziliare Perfusionsdruck beim Niedrigdruckglaukom. Fol Ophthalmol 13:333–337Google Scholar
  23. 23.
    Van Stokkum IHM (1992) User note. Solving a separable nonlinear least squares problem: parameter estimation of time resolved spectra. IMSL/Directions 9 1:10–11Google Scholar

Copyright information

© Springer-Verlag 1995

Authors and Affiliations

  • Ivo H. M. van Stokkum
    • 1
  • George N. Lambrou
    • 2
  • Tom J. T. P. van den Berg
    • 2
  1. 1.Faculty of Physics and AstronomyFree UniversityAmsterdamThe Netherlands
  2. 2.AMCLaboratory of Medical Physics and InformaticsAmsterdamThe Netherlands

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