Using the model-based residual bootstrap to quantify uncertainty in fiber orientations from Q-ball analysis.
Bootstrapping of repeated diffusion-weighted image datasets enables nonparametric quantification of the uncertainty in the inferred fiber orientation. The wild bootstrap and the residual bootstrap are model-based residual resampling methods which use a single dataset. Previously, the wild bootstrap method has been presented as an alternative to conventional bootstrapping for diffusion tensor imaging. Here we present a study of an implementation of model-based residual bootstrapping using q -ball analysis and compare the outputs with conventional bootstrapping. We show that model-based residual bootstrap q-ball generates results that closely match the output of the conventional bootstrap. Both the residual and conventional bootstrap of multifiber methods can be used to estimate the probability of different numbers of fiber populations existing in different brain tissues. Also, we have shown that these methods can be used to provide input for probabilistic tractography, avoiding existing limitations associated with data calibration and model selection.
Haroon
Hamied A
HA
Faculty of Medical and Human Sciences, The University of Manchester, M13 9PT Manchester, UK. hamied.haroon@manchester.ac.uk
Morris
David M
DM
Embleton
Karl V
KV
Alexander
Daniel C
DC
Parker
Geoffrey J M
GJ
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D009412:Nerve Fibers; D009434:Neural Pathways / Q000033:anatomy & histology; D010363:Pattern Recognition, Automated / Q000379:methods*