bibtext: @InProceedings{ Li_Surfacebased_MICCAI2024,
                   author = { Li, Yuan and Nie, Xinyu and Zhang, Jianwei and Shi, Yonggang },
                   title = { { Surface-based and Shape-informed U-fiber Atlasing for Robust Superficial White Matter Connectivity Analysis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15002 },
                   month = {October},
                   pages = { pending },
                }
abstract: Superficial white matter (SWM) U-fibers contain consider able structural connectivity in the human brain; however, related studies are not well-developed compared to the well-studied deep white matter (DWM). Conventionally, SWM U-fiber is obtained through DWM tracking, which is inaccurate on the cortical surface. The significant variability in the cortical folding patterns of the human brain renders a conventional template-based atlas unsuitable for accurately mapping U-fibers within the thin layer of SWM beneath the cortical surface. Recently, new surface-based tracking methods have been developed to reconstruct more complete and reliable U-fibers. To leverage surface-based U-fiber tracking methods, we propose to create a surface-based U-fiber dictionary using high-resolution diffusion MRI (dMRI) data from the Human Connectome Project (HCP). We first identify the major U-fiber bundles and then build a dictionary containing subjects with high groupwise consistency of major U-fiber bundles. Finally, we propose a shape-informed U-fiber atlasing method for robust SWM connectivity analysis. Through experiments, we demonstrate that our shape-informed atlasing method can obtain anatomically more accurate U-fiber representations than state of-the-art atlas. Additionally, our method is capable of restoring incomplete U-fibers in low-resolution dMRI, thus helping better characterize SWM connectivity in clinical studies such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
title: Surface-based and Shape-informed U-fiber Atlasing for Robust Superficial White Matter Connectivity Analysis
authors: Li, Yuan Nie, Xinyu Zhang, Jianwei Shi, Yonggang