58 citations · 167 across the 37 of their papers we have counts for
38 papers
Cross-Population White Matter Atlas Creation for Concurrent Mapping of Brain Connections in Neonates and Adults with Diffusion MRI Tractography
Wei Zhang, Yijie Li, Ruixi Zheng +9
Comparing white matter (WM) connections between adults and neonates using diffusion MRI (dMRI) can advance our understanding of typical brain development and potential biomarkers f…
DMVFC: Deep Learning Based Functionally Consistent Tractography Fiber Clustering Using Multimodal Diffusion MRI and Functional MRI
Bocheng Guo, Jin Wang, Yijie Li +9
Tractography fiber clustering using diffusion MRI (dMRI) is a crucial method for white matter (WM) parcellation to enable analysis of brains structural connectivity in health and d…
DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modeling
Yijie Li, Wei Zhang, Xi Zhu +4
This paper presents DDTracking, a novel deep generative framework for diffusion MRI tractography that formulates streamline propagation as a conditional denoising diffusion process…
AGFS-Tractometry: A Novel Atlas-Guided Fine-Scale Tractometry Approach for Enhanced Along-Tract Group Statistical Comparison Using Diffusion MRI Tractography
Ruixi Zheng, Wei Zhang, Yijie Li +7
Diffusion MRI (dMRI) tractography is currently the only method for in vivo mapping of the brain's white matter (WM) connections. Tractometry is an advanced tractography analysis te…
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography
Yui Lo, Yuqian Chen, Dongnan Liu +8
Shape measures have emerged as promising descriptors of white matter tractography, offering complementary insights into anatomical variability and associations with cognitive and c…
TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View
Yuqian Chen, Leo Zekelman, Yui Lo +7
Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical s…
DeepNuParc: A Novel Deep Clustering Framework for Fine-scale Parcellation of Brain Nuclei Using Diffusion MRI Tractography
Haolin He, Ce Zhu, Le Zhang +9
Brain nuclei are clusters of anatomically distinct neurons that serve as important hubs for processing and relaying information in various neural circuits. Fine-scale parcellation…
A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRI
Jin Wang, Bocheng Guo, Yijie Li +7
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…
MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI
Nancy R. Newlin, Kurt Schilling, Serge Koudoro +33
White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (…
TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds
Yui Lo, Yuqian Chen, Dongnan Liu +9
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…
The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
Yui Lo, Yuqian Chen, Dongnan Liu +9
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 acro…
DDEvENet: Evidence-based Ensemble Learning for Uncertainty-aware Brain Parcellation Using Diffusion MRI
Chenjun Li, Dian Yang, Shun Yao +14
In this study, we developed an Evidence-based Ensemble Neural Network, namely EVENet, for anatomical brain parcellation using diffusion MRI. The key innovation of EVENet is the des…
Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning
Ari Tchetchenian, Leo Zekelman, Yuqian Chen +8
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion MRI tractography parcellation methods have been succes…
White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging
Yui Lo, Yuqian Chen, Fan Zhang +6
Parcellation of white matter tractography provides anatomical features for disease prediction, anatomical tract segmentation, surgical brain mapping, and non-imaging phenotype clas…
TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractography
Yuqian Chen, Fan Zhang, Meng Wang +10
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of brain white ma…
A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations
Yijie Li, Wei Zhang, Ye Wu +11
The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental he…
Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction
Yui Lo, Yuqian Chen, Dongnan Liu +8
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 inter…
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
Yuxiang Wei, Yuqian Chen, Tengfei Xue +6
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.…
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography
Haolin He, Ce Zhu, Le Zhang +9
The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomi…
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
Tengfei Xue, Yuqian Chen, Chaoyi Zhang +6
Diffusion MRI tractography parcellation classifies streamlines into anatomical fiber tracts to enable quantification and visualization for clinical and scientific applications. Cur…
TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance
Yuqian Chen, Leo R. Zekelman, Chaoyi Zhang +8
We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise…
Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods
Jianzhong He, Fan Zhang, Yiang Pan +8
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. Diffusion MRI tractogra…
Fiber Tract Shape Measures Inform Prediction of Non-Imaging Phenotypes
Wan Liu, Yuqian Chen, Chuyang Ye +5
Neuroimaging measures of the brain's white matter connections can enable the prediction of non-imaging phenotypes, such as demographic and cognitive measures. Existing works have i…
TractGraphCNN: anatomically informed graph CNN for classification using diffusion MRI tractography
Yuqian Chen, Fan Zhang, Leo R. Zekelman +7
The structure and variability of the brain's connections can be investigated via prediction of non-imaging phenotypes using neural networks. However, known neuroanatomical relation…