Optimizing structure-function relationship by maximizing correspondence between glaucomatous visual fields and mathematical retinal nerve fiber models.

Purpose: To introduce a method to optimize structural retinal nerve fiber layer (RNFL) models based on glaucomatous visual field data and to show how such an optimized model can be used to reduce noise in visual fields while probably preserving clinically important features.

Methods: Correlation coefficients between age-adjusted deviation values of pairs of visual field test locations were calculated from 103 visual fields of eyes with moderate glaucomatous damage. Distances between those test locations were defined for various parameters of a mathematical RNFL model. Then, the correspondence between the structural and functional data was defined by the spread, or variance, of the correlation coefficients for all distances. The model parameters that minimized this spread constituted the optimized model. To reduce noise in visual fields, the optimized model was used to smooth visual field data according to the RNFL's structure. The resulting fields were compared with visual fields that were smoothed based on the regular testing grid.

Results: The optimal parameters for the RNFL model reduced the variance of the correlation coefficients by 78% and were well within the range of parameters previously determined from fundus photographs. Smoothing the visual fields based on the optimized RNFL model strongly reduced noise while keeping important features.

Conclusions: Mathematic RNFL models can be optimized based on visual field data, resulting in a strong structure-function relationship. Taking the RNFL's shape, as defined by such an optimized model, into account when smoothing visual fields results in better noise reduction while preserving important details.