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Real-time motion analytics during brain MRI improve data quality and reduce costs
Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring c...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5731481/ https://www.ncbi.nlm.nih.gov/pubmed/28803940 http://dx.doi.org/10.1016/j.neuroimage.2017.08.025 |
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author | Dosenbach, Nico U.F. Koller, Jonathan M. Earl, Eric A. Miranda-Dominguez, Oscar Klein, Rachel L. Van, Andrew N. Snyder, Abraham Z. Nagel, Bonnie J. Nigg, Joel T. Nguyen, Annie L. Wesevich, Victoria Greene, Deanna J. Fair, Damien A. |
author_facet | Dosenbach, Nico U.F. Koller, Jonathan M. Earl, Eric A. Miranda-Dominguez, Oscar Klein, Rachel L. Van, Andrew N. Snyder, Abraham Z. Nagel, Bonnie J. Nigg, Joel T. Nguyen, Annie L. Wesevich, Victoria Greene, Deanna J. Fair, Damien A. |
author_sort | Dosenbach, Nico U.F. |
collection | PubMed |
description | Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional ‘buffer data’, an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more. |
format | Online Article Text |
id | pubmed-5731481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
spelling | pubmed-57314812017-12-15 Real-time motion analytics during brain MRI improve data quality and reduce costs Dosenbach, Nico U.F. Koller, Jonathan M. Earl, Eric A. Miranda-Dominguez, Oscar Klein, Rachel L. Van, Andrew N. Snyder, Abraham Z. Nagel, Bonnie J. Nigg, Joel T. Nguyen, Annie L. Wesevich, Victoria Greene, Deanna J. Fair, Damien A. Neuroimage Article Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional ‘buffer data’, an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more. 2017-08-10 2017-11-01 /pmc/articles/PMC5731481/ /pubmed/28803940 http://dx.doi.org/10.1016/j.neuroimage.2017.08.025 Text en http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Dosenbach, Nico U.F. Koller, Jonathan M. Earl, Eric A. Miranda-Dominguez, Oscar Klein, Rachel L. Van, Andrew N. Snyder, Abraham Z. Nagel, Bonnie J. Nigg, Joel T. Nguyen, Annie L. Wesevich, Victoria Greene, Deanna J. Fair, Damien A. Real-time motion analytics during brain MRI improve data quality and reduce costs |
title | Real-time motion analytics during brain MRI improve data quality and reduce costs |
title_full | Real-time motion analytics during brain MRI improve data quality and reduce costs |
title_fullStr | Real-time motion analytics during brain MRI improve data quality and reduce costs |
title_full_unstemmed | Real-time motion analytics during brain MRI improve data quality and reduce costs |
title_short | Real-time motion analytics during brain MRI improve data quality and reduce costs |
title_sort | real-time motion analytics during brain mri improve data quality and reduce costs |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5731481/ https://www.ncbi.nlm.nih.gov/pubmed/28803940 http://dx.doi.org/10.1016/j.neuroimage.2017.08.025 |
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