A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images...

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Autores principales:	Mahbaneh Eshaghzadeh Torbati, Davneet S. Minhas, Ghasan Ahmad, Erin E. O’Connor, John Muschelli, Charles M. Laymon, Zixi Yang, Ann D. Cohen, Howard J. Aizenstein, William E. Klunk, Bradley T. Christian, Seong Jae Hwang, Ciprian M. Crainiceanu, Dana L. Tudorascu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	MRI Scanner effects Normalization Harmonization Alzheimer's disease Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/186c8ae68de040a5866157a2a3e971a1
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id	oai:doaj.org-article:186c8ae68de040a5866157a2a3e971a1
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spelling	oai:doaj.org-article:186c8ae68de040a5866157a2a3e971a12021-11-06T04:21:27ZA multi-scanner neuroimaging data harmonization using RAVEL and ComBat1095-957210.1016/j.neuroimage.2021.118703https://doaj.org/article/186c8ae68de040a5866157a2a3e971a12021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921009642https://doaj.org/toc/1095-9572Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images obtained from different scanners contain substantial technical biases). Here we evaluate and compare results of data analysis methods without any data transformation (RAW), with intensity normalization using RAVEL, with regional harmonization methods using ComBat, and a combination of RAVEL and ComBat. Methods are evaluated on a unique sample of 16 study participants who were scanned on both 1.5T and 3T scanners a few months apart. Neuroradiological evaluation was conducted for 7 different regions of interest (ROI's) pertinent to Alzheimer's disease (AD). Cortical measures and results indicate that: (1) RAVEL substantially improved the reproducibility of image intensities; (2) ComBat is preferred over RAVEL and the RAVEL-ComBat combination in terms of regional level harmonization due to more consistent harmonization across subjects and image-derived measures; (3) RAVEL and ComBat substantially reduced bias compared to analysis of RAW images, but RAVEL also resulted in larger variance; and (4) the larger root mean square deviation (RMSD) of RAVEL compared to ComBat is due mainly to its larger variance.Mahbaneh Eshaghzadeh TorbatiDavneet S. MinhasGhasan AhmadErin E. O’ConnorJohn MuschelliCharles M. LaymonZixi YangAnn D. CohenHoward J. AizensteinWilliam E. KlunkBradley T. ChristianSeong Jae HwangCiprian M. CrainiceanuDana L. TudorascuElsevierarticleMRIScanner effectsNormalizationHarmonizationAlzheimer's diseaseNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118703- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MRI Scanner effects Normalization Harmonization Alzheimer's disease Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	MRI Scanner effects Normalization Harmonization Alzheimer's disease Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Mahbaneh Eshaghzadeh Torbati Davneet S. Minhas Ghasan Ahmad Erin E. O’Connor John Muschelli Charles M. Laymon Zixi Yang Ann D. Cohen Howard J. Aizenstein William E. Klunk Bradley T. Christian Seong Jae Hwang Ciprian M. Crainiceanu Dana L. Tudorascu A multi-scanner neuroimaging data harmonization using RAVEL and ComBat
description	Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images obtained from different scanners contain substantial technical biases). Here we evaluate and compare results of data analysis methods without any data transformation (RAW), with intensity normalization using RAVEL, with regional harmonization methods using ComBat, and a combination of RAVEL and ComBat. Methods are evaluated on a unique sample of 16 study participants who were scanned on both 1.5T and 3T scanners a few months apart. Neuroradiological evaluation was conducted for 7 different regions of interest (ROI's) pertinent to Alzheimer's disease (AD). Cortical measures and results indicate that: (1) RAVEL substantially improved the reproducibility of image intensities; (2) ComBat is preferred over RAVEL and the RAVEL-ComBat combination in terms of regional level harmonization due to more consistent harmonization across subjects and image-derived measures; (3) RAVEL and ComBat substantially reduced bias compared to analysis of RAW images, but RAVEL also resulted in larger variance; and (4) the larger root mean square deviation (RMSD) of RAVEL compared to ComBat is due mainly to its larger variance.
format	article
author	Mahbaneh Eshaghzadeh Torbati Davneet S. Minhas Ghasan Ahmad Erin E. O’Connor John Muschelli Charles M. Laymon Zixi Yang Ann D. Cohen Howard J. Aizenstein William E. Klunk Bradley T. Christian Seong Jae Hwang Ciprian M. Crainiceanu Dana L. Tudorascu
author_facet	Mahbaneh Eshaghzadeh Torbati Davneet S. Minhas Ghasan Ahmad Erin E. O’Connor John Muschelli Charles M. Laymon Zixi Yang Ann D. Cohen Howard J. Aizenstein William E. Klunk Bradley T. Christian Seong Jae Hwang Ciprian M. Crainiceanu Dana L. Tudorascu
author_sort	Mahbaneh Eshaghzadeh Torbati
title	A multi-scanner neuroimaging data harmonization using RAVEL and ComBat
title_short	A multi-scanner neuroimaging data harmonization using RAVEL and ComBat
title_full	A multi-scanner neuroimaging data harmonization using RAVEL and ComBat
title_fullStr	A multi-scanner neuroimaging data harmonization using RAVEL and ComBat
title_full_unstemmed	A multi-scanner neuroimaging data harmonization using RAVEL and ComBat
title_sort	multi-scanner neuroimaging data harmonization using ravel and combat
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/186c8ae68de040a5866157a2a3e971a1
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A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

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