Fiber tractography (FT), which aims to reconstruct the three-dimensional trajectories of white matter (WM) fibers non-invasively, is one of the most popular approaches for analyzing diffusion tensor imaging (DTI) data given its high inter- and intra-rater reliability and scan-rescan reproducibility. The major disadvantage of manual FT segmentations, unfortunately, is that placing regions-of-interest for tract selection can be very labor-intensive and time-consuming. Although there are several methods that can identify specific WM fiber bundles in an automated way, manual FT segmentations across multiple subjects performed by a trained rater with neuroanatomical expertise are generally assumed to be more accurate. However, for longitudinal DTI analyses it may still be beneficial to automate the FT segmentation across multiple time points, but then for each individual subject separately. Both the inter-subject and intra-subject automation in this situation are intended for subjects without gross pathology. In this work, we propose such an automated longitudinal intra-subject analysis (dubbed ALISA) approach, and assessed whether ALISA could preserve the same level of reliability as obtained with manual FT segmentations. In addition, we compared ALISA with an automated inter-subject analysis. Based on DTI data sets from (i) ten healthy subjects that were scanned five times (six-month intervals, aged 7.6-8.6years at the first scan) and (ii) one control subject that was scanned ten times (weekly intervals, 12.2years at the first scan), we demonstrate that the increased efficiency provided by ALISA does not compromise the high degrees of precision and accuracy that can be achieved with manual FT segmentations. Further automation for inter-subject analyses, however, did not provide similarly accurate FT segmentations.
Neuroimage, 2014, Vol 86, p. 404-416
clinical assessment methods, lab technology and radiography; Tractography; Diffusion MRI; DTI; Longitudinal data analyses; Journal Article; Research Support, Non-U.S. Gov't