TY - JOUR
T1 - Optimizing full-brain coverage in human brain MRI through population distributions of brain size
AU - Mennes, Maarten
AU - Jenkinson, Mark
AU - Valabregue, Romain
AU - Buitelaar, Jan K.
AU - Beckmann, Christian
AU - Smith, Stephen
N1 - Funding Information:
The authors like to thank the past and present members of the Institute for Pediatric Neuroscience at the NYU Langone Medical Center for providing the NYU datasets; Daniel Margulies and Judy Kipping from the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig for their help including the Max Planck Datasets; and all the past and present members of the BIG and NeuroIMAGE consortia for the inclusion of their data. This work makes use of the BIG (Brain Imaging Genetics) database, first established in Nijmegen, The Netherlands, in 2007. This resource is now part of Cognomics ( www.cognomics.nl ), a joint initiative by researchers of the Donders Centre for Cognitive Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the Radboud University Medical Centre and the Max Planck Institute for Psycholinguistics in Nijmegen. The Cognomics Initiative is supported by the participating departments and centres and by external grants, i.e. the Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL), the Hersenstichting Nederland, and the Netherlands Organisation for Scientific Research (NWO). The NeuroIMAGE project was supported by NIH Grant R01MH62873 (to Stephen V. Faraone), NWO Large Investment Grant 1750102007010 and ZonMW Grant 60-60600-97-193 (to Jan Buitelaar), and grants from the Radboud University Nijmegen Medical Center, University Medical Center Groningen and Accare, and VU University Amsterdam. MM is supported by a Brain & Cognition grant ( 056-13-015 ) to Jan Buitelaar. We would also like to thank Markus Barth, Marcel Zwiers and Rasim Boyacioglu for helpful MR physics discussions. Finally, we would like to thank The Neuro Bureau for facilitating collaboration in action, thereby greatly accelerating the data inclusion rate for this study. Appendix A
PY - 2014/9
Y1 - 2014/9
N2 - 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 11. mm 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 142. mm, 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.
AB - 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 11. mm 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 142. mm, 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.
KW - FOV
KW - Field-of-view
KW - Neuroimaging
KW - Population brain size
UR - http://www.scopus.com/inward/record.url?scp=84904096733&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2014.04.030
DO - 10.1016/j.neuroimage.2014.04.030
M3 - Article
C2 - 24747737
AN - SCOPUS:84904096733
SN - 1053-8119
VL - 98
SP - 513
EP - 520
JO - NeuroImage
JF - NeuroImage
ER -