Citation

Prajapati HS, Merchant-Borna K, Bazarian JJ, Linte CA, Cahill ND. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2021; 2021: 3906-3911.

Copyright

(Copyright © 2021, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC46164.2021.9629871

PMID

34892086

Abstract

Significant longitudinal changes in metrics derived from diffusion weighted magnetic resonance (MR) images of the brain have been observed in athletes subject to repetitive non-concussive head injuries (RHIs). Accurate alignment of longitudinal scans of a subject is an important step in detecting and quantifying these changes. Currently, tools such as DSI Studio [1], FreeSurfer [2], and FSL [3] perform pairwise rigid registration of all scans in a longitudinal sequence to the first time-point scan (or to another reference scan or template). While the rigid transformations obtained using this strategy can be computed in a manner that enforces inverse consistency, for the case of three or more scans, the transformations are not transitive. This can lead to discrepancy in the rigid transformations that can be measured in physical units. Using a diffusion MRI dataset collected and analyzed as part of a larger study in [4], [5], [6], we illustrate this discrepancy, and we show how it can lead to uncertainty in local/regional estimates of diffusion metrics including fractional anistropy (FA), mean diffusivity (MD), and quantitatve anisotropy (QA). Additionally, we propose a method to perform transitive longitudinal rigid registration of a sequence of scans in a manner that guarantees that the discrepancy in the transformations will be eliminated.Clinical relevance- This paper establishes that standard processing pipelines for performing longitudinal analysis of diffusion MR images of the brain exhibit registration discrepancies that can be eliminated.

Language: en