Automated longitudinal intra-subject analysis (ALISA) for diffusion MRI tractography

Abstract

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 7.6-8.6 years at the first scan) and (ii) one control subject that was scanned ten times (weekly intervals, 12.2 years 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.

Trade-off between angular and spatial resolutions in in vivo fiber tractography

“…An isotropic 1.5 × 1.5 × 1.5 mm 3 seeding grid was used for all datasets. From these whole-brain tractography results, several fiber bundles were segmented: the arcuate fasciculus (AF), as described in Catani and Thiebaut de Schotten (2008);the corticospinal tracts (CSTs) that terminate in the primary motor cortex (M1) were segmented as described in Wakana et al (2007) and Aarnink et al (2014). The ROIs selecting M1 were 26 mm superior to the corpus callosum on the mid-sagittal slice;…”

“…the corticospinal tracts (CSTs) that terminate in the primary motor cortex (M1) were segmented as described in Wakana et al (2007) and Aarnink et al (2014). The ROIs selecting M1 were 26 mm superior to the corpus callosum on the mid-sagittal slice;…”

Joint reconstruction of white-matter pathways from longitudinal diffusion MRI data with anatomical priors

“…A similar approach has been applied to diffusion-weighted (DW) MRI and combined with tensor-based registration to align images within and across subjects for region-of-interest (ROI) analyses of diffusion measures (Keihaninejad et al, 2013). This approach can also be used to transfer ROIs from an atlas with the purpose of labeling tractography streamlines (Aarnink et al, 2013). …”

Discrete pre-processing step effects in registration-based pipelines, a preliminary volumetric study on T1-weighted images

“…Several studies [8, 9, 13, 14–19] have already investigated the effects of multiple software suites on a single dataset, finding that one often outperforms another in a certain regard. For instance, the FreeSurfer suite [20] is widely used for cortical and subcortical parcellation, yet recent studies have shown that Advanced Normalization Tools (ANTs) [21], a more recently developed suite of software, outperforms FreeSurfer in certain aspects [17, 18, 22].…”