Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/121625
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dc.contributor.author | Mennes, M. | - |
dc.contributor.author | Jenkinson, M. | - |
dc.contributor.author | Valabregue, R. | - |
dc.contributor.author | Buitelaar, J.K. | - |
dc.contributor.author | Beckmann, C. | - |
dc.contributor.author | Smith, S. | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | NeuroImage, 2014; 98:513-520 | - |
dc.identifier.issn | 1053-8119 | - |
dc.identifier.issn | 1095-9572 | - |
dc.identifier.uri | http://hdl.handle.net/2440/121625 | - |
dc.description.abstract | When defining an MRI protocol, brain researchers need to set multiple interdependent parameters that define repetition time (TR), voxel size, field-of-view (FOV), etc. Typically, researchers aim to image the full brain, making the expected FOV an important parameter to consider. Especially in 2D-EPI sequences, non-wasteful FOV settings are important to achieve the best temporal and spatial resolution. In practice, however, imperfect FOV size estimation often results in partial brain coverage for a significant number of participants per study, or, alternatively, an unnecessarily large voxel-size or number of slices to guarantee full brain coverage. To provide normative FOV guidelines we estimated population distributions of brain size in the x-, y-, and z-direction using data from 14,781 individuals. Our results indicated that 11mm in the z-direction differentiate between obtaining full brain coverage for 90% vs. 99.9% of participants. Importantly, we observed that rotating the FOV to optimally cover the brain, and thus minimize the number of slices needed, effectively reduces the required inferior-superior FOV size by ~5%. For a typical adult imaging study, 99.9% of the population can be imaged with full brain coverage when using an inferior-superior FOV of 142mm, assuming optimal slice orientation and minimal within-scan head motion. By providing population distributions for brain size in the x-, y-, and z-direction we improve the potential for obtaining full brain coverage, especially in 2D-EPI sequences used in most functional and diffusion MRI studies. We further enable optimization of related imaging parameters including the number of slices, TR and total acquisition time. | - |
dc.description.statementofresponsibility | Maarten Mennes, Mark Jenkinson, Romain Valabregue, Jan K. Buitelaara, Christian Beckmann, Stephen Smith | - |
dc.language.iso | en | - |
dc.publisher | Elsevier | - |
dc.rights | © 2014 Elsevier Inc. All rights reserved. | - |
dc.source.uri | http://dx.doi.org/10.1016/j.neuroimage.2014.04.030 | - |
dc.subject | Brain | - |
dc.subject | Humans | - |
dc.subject | Magnetic Resonance Imaging | - |
dc.subject | Diffusion Magnetic Resonance Imaging | - |
dc.subject | Demography | - |
dc.subject | Adolescent | - |
dc.subject | Adult | - |
dc.subject | Aged | - |
dc.subject | Aged, 80 and over | - |
dc.subject | Middle Aged | - |
dc.subject | Child | - |
dc.subject | Child, Preschool | - |
dc.subject | Female | - |
dc.subject | Male | - |
dc.subject | Young Adult | - |
dc.subject | Neuroimaging | - |
dc.title | Optimizing full-brain coverage in human brain MRI through population distributions of brain size | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.neuroimage.2014.04.030 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Jenkinson, M. [0000-0001-6043-0166] | - |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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