报告人：Dr. Yonggang Shi, Tenure-Track Assistant Professor of
报告摘要: In this talk, I will present our recent work on the automated analysis of multimodal MR images for large scale brain mapping. I will first present a suite of novel algorithms for mapping brain structures using intrinsic geometry. The key idea in our method is the use of Laplace-Beltrami (LB) eigenfunctions for modeling brain shapes, such as hippocampus and cortex. These tools have the advantage of being invariant to pose and scale variances, and robust to deformations from development and pathology. Using the LB eigenfunctions and topology-preserving evolution, we have developed a robust approach for surface reconstruction from segmented masks. This method can remove outliers while accurately retaining volume information. For the challenging problem of cortical surface reconstruction, we have developed a unified approach for the joint correction of geometric and topological outliers with the Reeb graph of LB eigenfunctions. By using the LB embeddings of surfaces, we have developed a novel and general approach for surface mapping via the optimization of their conformal metrics. Based on these cutting-edge algorithms for image and shape analysis, completely automated workflows have been created for the large scale analysis of brain morphometry. In our current research, these intrinsic modeling techniques are being extended to multimodal image analysis for the more accurate and robust mapping of brain structure and function. Using the reconstructed cortical surfaces, we have developed more accurate ways of normalizing cerebral blood perfusion (CBF) images with cortical thickness and area, and successfully applied them to map sex differences in brain development. For the analysis of brain connectivity, we developed a novel algorithm for fiber orientation distribution (FOD) reconstruction that can be applied to diffusion imaging data collected from a wide range of acquisition schemes. With the help of FODs and intrinsic analysis, we are able to automatically extract fiber bundles with significantly improved details and robustness using the state-of-the-art data from the Human Connectome Project.
报告人简介: Dr. Yonggang Shi received his Bachelor and Master degree in Electrical Engineering from the Southeast University of China in 1996 and 1999 respectively. He received his Ph.D. in Electrical Engineering from