Analytical model of scanning laser polarimetry for retinal nerve fiber layer assessment

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Abstract

PURPOSE. To develop a quantitative understanding of scanning laser polarimetry (SLP) for retinal nerve fiber layer (RNFL) assessment in glaucoma diagnosis and management. METHODS. The Mueller calculus was used to model the polarization optics of SLP. A birefringent retinal structure (RNFL or macula) was represented as a circularly symmetric linear retarder with a radial slow axis. The birefringent cornea and a corneal compensator within the SLP instrument were represented as fixed linear retarders. The model provided images of the radial retarder that were compared with retardance images obtained by SLP of the macula in eight normal subjects. Theoretical and experimental images were quantified with circular profiles around the center of the radial retarder or macula. Experimental retardance profiles were varied by tilting the subject's head to rotate the corneal axis. The SLP model was fit to the experimental profiles by nonlinear least-squares curve fitting. RESULTS. The combined retarder formed by the cornea and corneal compensator induced bow-tie patterns in images of the radial retarder. Macular SLP images exhibited similar patterns. Retardance profiles could be characterized by three parameters: modulation, mean, and axis. The SLP model fit the experimental profiles very well (r2 = 0.8-0.9). CONCLUSIONS. The SLP model provided a quantitative framework within which to interpret SLP studies. Modulation-based parameters were generally more sensitive to retinal birefringence than mean-based parameters. Corneal birefringence is an important source of variance in SLP, especially for mean-based parameters. The theory developed for this study may guide improvements in clinical SLP.

Original languageEnglish (US)
Pages (from-to)383-392
Number of pages10
JournalInvestigative Ophthalmology and Visual Science
Volume43
Issue number2
StatePublished - Feb 11 2002

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ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience

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