Coupled parametric model for estimation of visual field tests based on OCT macular thickness maps, and vice versa, in glaucoma care.

The current standard of care for glaucoma patients consists of functional assessment of vision via visual field (VF) testing, which is sensitive but subjective, time-consuming, and often unreliable. A new imaging technology, Fourier domain optical coherence tomography (OCT), is being introduced to assess the structural characteristics of the macula. This new complementary exam is efficient, objective, and reliable. A complex, but consistent, relationship exists between the structural information provided by macular OCT and the functional information gathered by VF maps. We propose a learning-based framework with the ability to estimate the VF map based on OCT macular thickness measurements as input (and vice versa). Central to this algorithmic framework is a coupled parametric model that captures not only the individual variabilities of the OCT macular thickness measurements and the VF maps, but also their co-dependencies. This model is derived by applying principal component analysis (PCA) to a library consisting of various pairs of OCT and VF maps. The parameters of this coupled model are obtained by solving a linear least squares problem. Next, these estimated parameters are used, in conjunction with the eigenvectors derived from PCA, to compute the estimate. The accuracy of this coupled parametric estimation model was evaluated by performing multiple leave-one-out cross validation experiments.