Hi UKB Imaging Team--
Our goal is to plug FSL-VBM-ed Jacobian maps made from UKB T1-weighted data into an age-prediction convolutional neural net. Typically, we like to do visual quality assurance of the template normalized brain data for such an endeavor but this gets a little burdensome for ~48,000 volumes. Would UKB Data-Field 25732 (Discrepancy between T1 brain image and standard-space brain template (nonlinearly-aligned)) be up to the task of providing us with a quick and dirty metric of normalization quality? If so, is there a recommended cut-off value for keeping versus culling data? Alternatively, since we'll be running FSL-VBM on all the data, is there an option we can specify to provide some quantitative QA that we can make use of?
Many thanks!
Paul and Robin
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