TU-CD-BRB-05: Radiation Damage Signature of White Matter Fiber Bundles Using Diffusion Tensor Imaging (DTI)

Authors


Abstract

Purpose:

To develop an automated and scalable approach and identify temporal, spatial and dosimetric patterns of radiation damage of white matter (WM) fibers following partial brain irradiation.

Methods:

An automated and scalable approach was developed to extract DTI features of 22 major WM fibers from 33 patients with low-grade/benign tumors treated by radiation therapy (RT). DTI scans of the patients were performed pre-RT, 3- and 6-week during RT, and 1, 6 and 18 months after RT. The automated tractography analysis was applied to 198 datasets as: (1) intra-subject registration of longitudinal DTI, (2) spatial normalization of individual-patient DTI to the Johns Hopkins WM Atlas, (3) automatic fiber tracking regulated by the WM Atlas, and (4) segmentation of WM into 22 major tract profiles. Longitudinal percentage changes in fractional anisotropy (FA), and mean, axial and radial diffusivity (MD/AD/RD) of each tract from pre-RT were quantified and correlated to 95%, 90% and 80% percentiles of doses and mean doses received by the tract. Heatmaps were used to identify clusters of significant correlation and reveal temporal, spatial and dosimetric signatures of WM damage. A multivariate linear regression was further carried out to determine influence of clinical factors.

Results:

Of 22 tracts, AD/MD changes in 12 tracts had significant correlation with doses, especially at 6 and 18 months post-RT, indicating progressive radiation damage after RT. Most interestingly, the DTI-index changes in the elongated tracts were associated with received maximum doses, suggesting a serial-structure behavior; while short association fibers were affected by mean doses, indicating a parallel-structure response.

Conclusion:

Using an automated DTI-tractography analysis of whole brain WM fibers, we reveal complex radiation damage patterns of WM fibers. Damage in WM fibers that play an important role in the neural network could be associated with late neurocognitive function declines after brain irradiation.

NIH NS064973

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