Dynamic Programming Definition of Boundaries of the Planum Temporale

Nayoung Lee*, J. Tilak Ratnanather†, Patrick Barta‡, Monica Hurdal§, Michael Miller†

*Dept of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
†Center for Imaging Science, Johns Hopkins University, MD 21218
‡Dept of Psychiatry, Johns Hopkins University School of Medicine, MD 21205
§Dept of Mathematics, Florida State University, Tallahassee, FL 32306

Modeling & Analysis

Abstract

Dynamic programming was used to define boundaries of cortical submanifolds with focus on the planum temporale (PT) of the superior temporal gyrus (STG), a region implicated in a variety of neuropsychiatric disorders. To this end, automated methods were used to generate the cortical surface of PT from 10 high resolution MRI subvolume encompassing the STG. Bayesian segmentation was then used to segment the subvolumes into cerebrospinal fluid, gray matter (GM) and white matter (WM).

3D-isocontouring using the intensity value at which there is equal probability of GM and WM is used to reconstruct the triangulated graph representing the STG cortical surface, enabling principal curvature at each point on the graph to be computed. Dynamic programming was used to delineate the PT cortical surface by tracking principal curves from the retro-insular end of the Heschl's Gyrus (HG) to the STG, along the posterior STG up to the start of the ramus and back to the retro-insular end of the HG. A coordinate system was then defined on the PT cortical surface. The origin was defined by the retro-insular end of the HG and the y-axis passes through the point on the posterior STG where the ramus begins.

Automated labelling of GM in the STG is robust with probability of misclassification of gray matter voxels between Bayesian and manual segmentation in the range 0.001-0.12 (n=20). PT reconstruction is also robust with 90% of the vertices of the reconstructed PT within 1 mm (n=20) from semi-automated contours. Finally, the inter-rater reliability for the surface area derived from repeated reconstructions was 0.96 for the left PT and 0.94 for the right PT, thus demonstrating the robustness of dynamic programming in defining a coordinate system on the PT. It provides a method with potential significance in the study of neuropsychiatric disorders.

Joint work with N. Honeycutt and G. Pearlson. Research supported by NIH P41-RR15241, MH 43775, MH 60504 and MH 52886 and NSF FRG DMS-0101329.