A novel approach to identifying poorly resolved boundaries between adjacent sulcal cortical banks in MR images
of the human brain is presented. The algorithm calculates an electrostatic potential field in a partial differential
equation (PDE) model of an inhomogeneous dielectric layer of gray matter that surrounds conductive white
matter. Correspondence trajectories and geodesic distances are computed along the streamlines of the potential
field gradient using PDEs in a Eulerian framework. The skeleton of a sulcal medial boundary is identified by a
simple procedure that finds irregularities/collisions in the field of correspondences. The skeleton detection procedure
is robust to noise, does not produce spurious artifacts and does not require tunable parameters. Results
of the algorithm are compared with a closely related technique, called Anatomically Consistent Enhancement
(ACE) (Han et al. CRUISE: Cortical reconstruction using implicit surface evolution, 2004). Results demonstrate
that the approach proposed here has a number of advantages over ACE and produces skeletons with a
more regular structure. This algorithm was developed as a part of a more general PDE-based framework for
cortical reconstruction, which integrates the potential field gradient flow and the skeleton barriers into a level set
deformable model. This technique is primarily aimed at anatomically consistent and accurate reconstruction of
cortical surface models in the presence of imaging noise and partial volume effects, but the identified intrasulcal
medial surfaces can serve other purposes as well, e.g. as landmarks in nonrigid registration, or as sulcal ribbons
that characterize the cortical folding.
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