Abstract
Lesion analyses are critical for drawing insights about stroke injury and recovery, and their importance is underscored by growing efforts to collect and combine stroke neuroimaging data across research sites. However, while there are numerous processing pipelines for neuroimaging data in general, few can be smoothly applied to stroke data due to complications analyzing the lesioned region. As researchers often use their own tools or manual methods for stroke MRI analysis, this could lead to greater errors and difficulty replicating findings over time and across sites. Rigorous analysis protocols and quality control pipelines are thus urgently needed for stroke neuroimaging. To this end, we created the Pipeline for Analyzing Lesions after Stroke (PALS; DOI: https://doi.org/10.5281/zenodo.1266980), a scalable and user-friendly toolbox to facilitate and ensure quality in stroke research specifically using T1-weighted MRIs. The PALS toolbox offers four modules integrated into a single pipeline, including (1) reorientation to radiological convention, (2) lesion correction for healthy white matter voxels, (3) lesion load calculation, and (4) visual quality control. In the present paper, we discuss each module and provide validation and example cases of our toolbox using multi-site data. Importantly, we also show that lesion correction with PALS significantly improves similarity between manual lesion segmentations by different tracers (z = 3.43, p = 0.0018). PALS can be found online at https://github.com/npnl/PALS. Future work will expand the PALS capabilities to include multimodal stroke imaging. We hope PALS will be a useful tool for the stroke neuroimaging community and foster new clinical insights.
Highlights
Characterizing the relationship between brain structure and function is an important step in identifying and targeting biomarkers of recovery after stroke (Dimyan and Cohen, 2011)
We found a significant difference among the inter-rater dice correlation coefficient (DC) scores (F = 5.91, p = 0.0183); Tukey post hoc comparisons with Bonferroni correction showed that inter-rater DC scores, the average number of voxels overlapping between manual segmentations of the tracers, were significantly higher after lesion correction with Pipeline for Analyzing lesion mask (Lesion) after Stroke (PALS) compared to lesion masks without any adjustment (z = 3.43, p = 0.0018); other pairwise comparisons did not reach significance (p > 0.18)
We showed that PALS increases inter-rater reliability of manual tracings: applying the lesion correction module in PALS significantly increased similarity between manually segmented lesions compared to no lesion correction and our previous version of lesion correction from the SRQL toolbox
Summary
Characterizing the relationship between brain structure and function is an important step in identifying and targeting biomarkers of recovery after stroke (Dimyan and Cohen, 2011). If the user chooses to run the Lesion Correction and/or Lesion Load Calculation modules, they are given the option to include the following files in each Subject Directory: a brain mask file (NifTI) and a white matter segmentation file (NifTI). If these steps have already been performed, brain extraction and white matter segmentation can be skipped during subsequent analyses. This demonstrates the susceptibility of larger, cortical strokes to image processing errors and highlights the importance of quality control
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