Bundle-specific tractogram distribution estimation using higher-order streamline differential equation

• Published in: NeuroImage (Orlando, Fla.) Vol. 298; p. 120766
• Main Authors: Feng, Yuanjing, Xie, Lei, Wang, Jingqiang, Tian, Qiyuan, He, Jianzhong, Zeng, Qingrun, Gao, Fei
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-09-2024; Elsevier Limited; Elsevier
• Subjects: Algorithms; Brain - anatomy & histology; Brain - diagnostic imaging; Bundle-specific tractogram distribution; Connectome - methods; Datasets; Diffusion MRI; Diffusion Tensor Imaging - methods; Embedded systems; Geometry; High-order streamline differential equation; Humans; Image Processing, Computer-Assisted - methods; Information processing; Kalman filters; Neural Pathways - anatomy & histology; Neural Pathways - diagnostic imaging; Optimization; Tractography; White Matter - anatomy & histology; White Matter - diagnostic imaging; Diffusion MRI; High-order streamline differential equation; Tractography; Bundle-specific tractogram distribution
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2024.120766

Streamline tractography locally traces peak directions extracted from fiber orientation distribution (FOD) functions, lacking global information about the trend of the whole fiber bundle. Therefore, it is prone to producing erroneous tracks while missing true positive connections. In this work, we propose a new bundle-specific tractography (BST) method based on a bundle-specific tractogram distribution (BTD) function, which directly reconstructs the fiber trajectory from the start region to the termination region by incorporating the global information in the fiber bundle mask. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion vectorial field. At the global level, the tractography process is simplified as the estimation of BTD coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) for qualitative and quantitative evaluation. Results demonstrate that our approach reconstructs complex fiber geometry more accurately. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts.
•A novel BTD function for fiber tractography to directly reconstruct the fiber trajectory is proposed.•A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion tensor vector field.•The fiber bundles are parameterized using the BTD coefficients, which are estimated by combining the priors and minimizing the energy on the diffusion tensor vector field.•Experimental results on Hough, Sine, Circle, FiberCup, ISMRM 2015 data, and HCP dataset show that the BTD is capable of reconstructing complex fiber bundles with long distances, large twists, and fan-shaped bundles, and shows better spatial consistency with the fiber geometry.