Zobrazeno 1 - 10
of 1 148
pro vyhledávání: '"MAKRIS, NIKOS"'
Autor:
Wang, Jin, Guo, Bocheng, Li, Yijie, Wang, Junyi, Chen, Yuqian, Rushmore, Jarrett, Makris, Nikos, Rathi, Yogesh, O'Donnell, Lauren J, Zhang, Fan
Tractography fiber clustering using diffusion MRI (dMRI) is a crucial strategy for white matter (WM) parcellation. Current methods primarily use the geometric information of fibers (i.e., the spatial trajectories) to group similar fibers into cluster
Externí odkaz:
http://arxiv.org/abs/2411.01859
Autor:
Lo, Yui, Chen, Yuqian, Liu, Dongnan, Legarreta, Jon Haitz, Zekelman, Leo, Zhang, Fan, Rushmore, Jarrett, Rathi, Yogesh, Makris, Nikos, Golby, Alexandra J., Cai, Weidong, O'Donnell, Lauren J.
Brain imaging studies have demonstrated that diffusion MRI tractography geometric shape descriptors can inform the study of the brain's white matter pathways and their relationship to brain function. In this work, we investigate the possibility of ut
Externí odkaz:
http://arxiv.org/abs/2410.22099
Autor:
Lo, Yui, Chen, Yuqian, Liu, Dongnan, Liu, Wan, Zekelman, Leo, Rushmore, Jarrett, Zhang, Fan, Rathi, Yogesh, Makris, Nikos, Golby, Alexandra J., Cai, Weidong, O'Donnell, Lauren J.
The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tracto
Externí odkaz:
http://arxiv.org/abs/2410.15108
Autor:
Tchetchenian, Ari, Zekelman, Leo, Chen, Yuqian, Rushmore, Jarrett, Zhang, Fan, Yeterian, Edward H., Makris, Nikos, Rathi, Yogesh, Meijering, Erik, Song, Yang, O'Donnell, Lauren J.
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion MRI tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely o
Externí odkaz:
http://arxiv.org/abs/2407.15132
Autor:
Chen, Yuqian, Zhang, Fan, Wang, Meng, Zekelman, Leo R., Cetin-Karayumak, Suheyla, Xue, Tengfei, Zhang, Chaoyi, Song, Yang, Makris, Nikos, Rathi, Yogesh, Cai, Weidong, O'Donnell, Lauren J.
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network desi
Externí odkaz:
http://arxiv.org/abs/2407.08883
Autor:
Li, Yijie, Zhang, Wei, Wu, Ye, Yin, Li, Zhu, Ce, Chen, Yuqian, Cetin-Karayumak, Suheyla, Cho, Kang Ik K, Zekelman, Leo R., Rushmore, Jarrett, Rathi, Yogesh, Makris, Nikos, O'Donnell, Lauren J., Zhang, Fan
The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assess
Externí odkaz:
http://arxiv.org/abs/2404.04604
Autor:
Lo, Yui, Chen, Yuqian, Liu, Dongnan, Liu, Wan, Zekelman, Leo, Zhang, Fan, Rathi, Yogesh, Makris, Nikos, Golby, Alexandra J., Cai, Weidong, O'Donnell, Lauren J.
Shape plays an important role in computer graphics, offering informative features to convey an object's morphology and functionality. Shape analysis in brain imaging can help interpret structural and functionality correlations of the human brain. In
Externí odkaz:
http://arxiv.org/abs/2403.19001
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
Autor:
Wei, Yuxiang, Chen, Yuqian, Xue, Tengfei, Zekelman, Leo, Makris, Nikos, Rathi, Yogesh, Cai, Weidong, Zhang, Fan, Donnell, Lauren J. O'
Large datasets often contain multiple distinct feature sets, or views, that offer complementary information that can be exploited by multi-view learning methods to improve results. We investigate anatomical multi-view data, where each brain anatomica
Externí odkaz:
http://arxiv.org/abs/2401.04579
Autor:
Xue, Tengfei, Chen, Yuqian, Zhang, Chaoyi, Golby, Alexandra J., Makris, Nikos, Rathi, Yogesh, Cai, Weidong, Zhang, Fan, O'Donnell, Lauren J.
Diffusion MRI tractography parcellation classifies streamlines into anatomical fiber tracts to enable quantification and visualization for clinical and scientific applications. Current tractography parcellation methods rely heavily on registration, b
Externí odkaz:
http://arxiv.org/abs/2307.09000
Autor:
Chen, Yuqian, Zekelman, Leo R., Zhang, Chaoyi, Xue, Tengfei, Song, Yang, Makris, Nikos, Rathi, Yogesh, Golby, Alexandra J., Cai, Weidong, Zhang, Fan, O'Donnell, Lauren J.
We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud repres
Externí odkaz:
http://arxiv.org/abs/2307.03982