Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique.

The lateral geniculate nucleus (LGN) is a small, inhomogeneous structure that relays major sensory inputs from the retina to the visual cortex. LGN morphology has been intensively studied due to various retinal diseases, as well as in the context of normal brain development. However, many of the methods used for LGN structural evaluations have not adequately addressed the challenges presented by the suboptimal routine MRI imaging of this structure. Here, we propose a novel method of edge enhancement that allows for high reliability and accuracy with regard to LGN morphometry, using routine 3D-MRI imaging protocols. This new algorithm is based on modeling a small brain structure as a polyhedron with its faces, edges, and vertices fitted with one plane, the intersection of two planes, and the intersection of three planes, respectively. This algorithm dramatically increases the contrast-to-noise ratio between the LGN and its surrounding structures as well as doubling the original spatial resolution. To show the algorithm efficacy, two raters (MA and ML) measured LGN volumes bilaterally in 19 subjects using the edge-enhanced LGN extracted areas from the 3D-T1 weighted images. The averages of the left and right LGN volumes from the two raters were 175 ± 8 and 174 ± 9 mm