An image-processing toolset for diffusion tensor tractography

Abstract

Diffusion tensor imaging (DTI)-based fiber tractography holds great promise in delineating neuronal fiber tracts and, hence, providing connectivity maps of the neural networks in the human brain. An array of image-processing techniques has to be developed to turn DTI tractography into a practically useful tool. To this end, we have developed a suite of image-processing tools for fiber tractography with improved reliability. This article summarizes the main technical developments we have made to date, which include anisotropic smoothing, anisotropic interpolation, Bayesian fiber tracking and automatic fiber bundling. A primary focus of these techniques is the robustness to noise and partial volume averaging, the two major hurdles to reliable fiber tractography. Performance of these techniques has been comprehensively examined with simulated and in vivo DTI data, demonstrating improvements in the robustness and reliability of DTI tractography.

Fig. 1

Effect of anisotropic smoothing on simulated data. (A) Middle portion of the noiseless simulated data. The line segments represent the PDD, and different colors denote different orientations of “fiber” bundles. (B) Enlarged view of the black boxed region in Panel A. (C) PDDs after adding zero mean Gaussian noise to simulated data (S.D. = 0.10). (D) PDDs after 60 iterations of anisotropic smoothing.

Fig. 2

Demonstration of the effect of anisotropic smoothing on two selected regions in the human DTI data. (A) A T₂-weighted image of a slice of the human brain. (B and E) In-plane component of PDDs in the boxed regions in Panel A. (C and F) PDDs after adding zero mean Gaussian noise (S.D. = 0.10) to the image data for the region in Panels B and E. (D and G) PDDs after 60 iterations of anisotropic smoothing.

Fig. 3

Anatomic map of a T₂-weighted monkey brain image (A). Effect of anisotropic smoothing after 25 iterations (B) and Gaussian smoothing after 3 iterations (C).

Fig. 4

(A) Profiles of 1-D sigmoid function with different values of shape control parameter a and (B) their corresponding frequency responses. Line styles denote different values of a (and are the same in both panels).

Fig. 5

“Fiber” tracts reconstructed from the synthetic spirals. Panels A–D are from linear, nearest-neighbor, cubic polynomial and spline interpolations, respectively. Panels E–H are from the anisotropic interpolation with a_max = 5, 10, 15 and 20, respectively.

Fig. 6

Axial view of reconstructed fibers seeded in the corpus callosum. Panels A–D are from linear, nearest-neighbor, cubic polynomial and spline interpolations, respectively, and Panels E–H are from the anisotropic interpolation with a_max = 5, 10, 15 and 20, respectively. Arrows in Panels A and B point to missed fibers, and arrows in Panels C, D and H point to possibly wrong connections.

Fig. 7

Front (A and B) and side (C and D) views of fibers tracked from synthetic data at SNR of 30 (A and C) and 20 (B and D) with a PVA region around the middle portion of the straight fibers. Red and blue curves are fibers from the Euler and Bayesian methods, respectively, and black curves are the “true fibers”. Four seed points are denoted with black dots in Panels A and B. The black line segments denote the direction of the major eigenvector at each voxel. Note the expanded scale of the abscissa in Panels C and D.

Fig. 8

Axial (A and B) and sagittal (C and D) views of reconstructed fiber tracts seeded in the superior longitudinal fasciculus from the original high-quality data. The left column (A and C) shows the fibers using the Euler method (linear interpolation), and the right column (B and D) shows fibers using the Bayesian method (anisotropic interpolation). Green arrows point to erroneous pathways using the Euler method.

Fig. 9

Axial (A and B) and sagittal (C and D) views of reconstructed fiber tracts seeded in the superior longitudinal fasciculus from noisy data (SNR = 20). The left column (A and C) shows the fibers using the Euler method (linear interpolation), and the right column (B and D) shows fibers using the Bayesian method (anisotropic interpolation). Green arrows point to additional erroneous pathway using the Euler method.

Fig. 10

Definition of the corresponding segment. P_iQ_i of F_i is the corresponding segment to P_jQ_j of F_j. See text for explanations.

Fig. 11

A bundle of fibers connecting superior to posterior corona radiata (A) using the Euler method. The green arrow shows an erroneous connection to the left side of the bundle, which could be effectively bundled into two groups. The major fiber bundle connecting the two regions is shown in Panel B.

Fig. 12

The original unclassified bundle (A) is separated into two distinct groups (B and C) using the bundling algorithm.