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Recommendations for Processing Head CT Data

Many research applications of neuroimaging use magnetic resonance imaging (MRI). As such, recommendations for image analysis and standardized imaging pipelines exist. Clinical imaging, however, relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Currently, there is on...

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Autor principal: Muschelli, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738271/
https://www.ncbi.nlm.nih.gov/pubmed/31551745
http://dx.doi.org/10.3389/fninf.2019.00061
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author Muschelli, John
author_facet Muschelli, John
author_sort Muschelli, John
collection PubMed
description Many research applications of neuroimaging use magnetic resonance imaging (MRI). As such, recommendations for image analysis and standardized imaging pipelines exist. Clinical imaging, however, relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Currently, there is only one image processing pipeline for head CT, which focuses mainly on head CT data with lesions. We present tools and a complete pipeline for processing CT data, focusing on open-source solutions, that focus on head CT but are applicable to most CT analyses. We describe going from raw DICOM data to a spatially normalized brain within CT presenting a full example with code. Overall, we recommend anonymizing data with Clinical Trials Processor, converting DICOM data to NIfTI using dcm2niix, using BET for brain extraction, and registration using a publicly-available CT template for analysis.
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spelling pubmed-67382712019-09-24 Recommendations for Processing Head CT Data Muschelli, John Front Neuroinform Neuroscience Many research applications of neuroimaging use magnetic resonance imaging (MRI). As such, recommendations for image analysis and standardized imaging pipelines exist. Clinical imaging, however, relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Currently, there is only one image processing pipeline for head CT, which focuses mainly on head CT data with lesions. We present tools and a complete pipeline for processing CT data, focusing on open-source solutions, that focus on head CT but are applicable to most CT analyses. We describe going from raw DICOM data to a spatially normalized brain within CT presenting a full example with code. Overall, we recommend anonymizing data with Clinical Trials Processor, converting DICOM data to NIfTI using dcm2niix, using BET for brain extraction, and registration using a publicly-available CT template for analysis. Frontiers Media S.A. 2019-09-04 /pmc/articles/PMC6738271/ /pubmed/31551745 http://dx.doi.org/10.3389/fninf.2019.00061 Text en Copyright © 2019 Muschelli. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Muschelli, John
Recommendations for Processing Head CT Data
title Recommendations for Processing Head CT Data
title_full Recommendations for Processing Head CT Data
title_fullStr Recommendations for Processing Head CT Data
title_full_unstemmed Recommendations for Processing Head CT Data
title_short Recommendations for Processing Head CT Data
title_sort recommendations for processing head ct data
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738271/
https://www.ncbi.nlm.nih.gov/pubmed/31551745
http://dx.doi.org/10.3389/fninf.2019.00061
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