Pipeline for Analyzing Lesions After Stroke (PALS)
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, f...
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doaj-43915fbba5c34072ac1cac9de2c7a0cb2020-11-25T00:25:44ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962018-09-011210.3389/fninf.2018.00063405014Pipeline for Analyzing Lesions After Stroke (PALS)Kaori L. Ito0Amit Kumar1Artemis Zavaliangos-Petropulu2Artemis Zavaliangos-Petropulu3Steven C. Cramer4Sook-Lei Liew5Sook-Lei Liew6Neural Plasticity and Neurorehabilitation Laboratory, University of Southern California, Los Angeles, CA, United StatesNeural Plasticity and Neurorehabilitation Laboratory, University of Southern California, Los Angeles, CA, United StatesNeural Plasticity and Neurorehabilitation Laboratory, University of Southern California, Los Angeles, CA, United StatesImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United StatesDepartment of Neurology, University of California, Irvine, Irvine, CA, United StatesNeural Plasticity and Neurorehabilitation Laboratory, University of Southern California, Los Angeles, CA, United StatesImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United StatesLesion 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.https://www.frontiersin.org/article/10.3389/fninf.2018.00063/fullstrokebig datalesion analysislesion loadMRI imagingneuroimaging |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kaori L. Ito Amit Kumar Artemis Zavaliangos-Petropulu Artemis Zavaliangos-Petropulu Steven C. Cramer Sook-Lei Liew Sook-Lei Liew |
spellingShingle |
Kaori L. Ito Amit Kumar Artemis Zavaliangos-Petropulu Artemis Zavaliangos-Petropulu Steven C. Cramer Sook-Lei Liew Sook-Lei Liew Pipeline for Analyzing Lesions After Stroke (PALS) Frontiers in Neuroinformatics stroke big data lesion analysis lesion load MRI imaging neuroimaging |
author_facet |
Kaori L. Ito Amit Kumar Artemis Zavaliangos-Petropulu Artemis Zavaliangos-Petropulu Steven C. Cramer Sook-Lei Liew Sook-Lei Liew |
author_sort |
Kaori L. Ito |
title |
Pipeline for Analyzing Lesions After Stroke (PALS) |
title_short |
Pipeline for Analyzing Lesions After Stroke (PALS) |
title_full |
Pipeline for Analyzing Lesions After Stroke (PALS) |
title_fullStr |
Pipeline for Analyzing Lesions After Stroke (PALS) |
title_full_unstemmed |
Pipeline for Analyzing Lesions After Stroke (PALS) |
title_sort |
pipeline for analyzing lesions after stroke (pals) |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroinformatics |
issn |
1662-5196 |
publishDate |
2018-09-01 |
description |
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. |
topic |
stroke big data lesion analysis lesion load MRI imaging neuroimaging |
url |
https://www.frontiersin.org/article/10.3389/fninf.2018.00063/full |
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