Estimating white matter fiber pathways from a diffusion tensor MRI dataset has many important applications in medical research. However, the standard approach of performing tracking on single-tensor estimates per voxel is confounded by regions of multiple pathways in different directions. Building on previous work for estimating multiple tensors from MR value partitioning, we present here a two-tensor fiber tractography method that estimates two tensors from the acquired MR values, interpolated at each step of the path, and follows the tensor most aligned with the current direction. The method is verified on a synthetic dataset and applied to two locations of fiber crossing in an in vivo diffusion MRI.

We describe a technique that uses tractography to visualize neural pathways in human brains by extending an existing framework that uses overlapping Gaussian tensors to model the signal. At each point on the fiber, an unscented Kalman filter is used to find the most consistent direction as a mixture of previous estimates and of the local model. In our previous framework, the diffusion ellipsoid had a cylindrical shape, i.e., the diffusion tensor’s second and third eigenvalues were identical. In this paper, we extend the tensor representation so that the diffusion tensor is represented by an arbitrary ellipsoid. Experiments on synthetic data show a reduction in the angular error at fiber crossings and branchings. Tests on in vivo data demonstrate the ability to trace fibers in areas containing crossings or branchings, and the tests also confirm the superiority of using a full tensor representation over the simplified model.

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