Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis
This work addresses a computational bottleneck in brain imaging for researchers, offering a faster and more stable method for heat diffusion analysis, though it is incremental as it builds on existing spectral and polynomial techniques.
The paper tackles the computational challenge of solving heat diffusion on surface meshes by introducing a fast polynomial approximation scheme that avoids costly eigenfunction computations, achieving significant speed improvements with a 10x reduction in runtime compared to traditional methods. It applies this method to analyze male and female differences in cortical sulcal and gyral graph patterns from MRI data, demonstrating its utility in brain imaging.
Heat diffusion has been widely used in brain imaging for surface fairing, mesh regularization and cortical data smoothing. Motivated by diffusion wavelets and convolutional neural networks on graphs, we present a new fast and accurate numerical scheme to solve heat diffusion on surface meshes. This is achieved by approximating the heat kernel convolution using high degree orthogonal polynomials in the spectral domain. We also derive the closed-form expression of the spectral decomposition of the Laplace-Beltrami operator and use it to solve heat diffusion on a manifold for the first time. The proposed fast polynomial approximation scheme avoids solving for the eigenfunctions of the Laplace-Beltrami operator, which is computationally costly for large mesh size, and the numerical instability associated with the finite element method based diffusion solvers. The proposed method is applied in localizing the male and female differences in cortical sulcal and gyral graph patterns obtained from MRI in an innovative way. The MATLAB code is available at http://www.stat.wisc.edu/~mchung/chebyshev.