ABSTRACT
Nonlinearities of gradient magnetic fields in diffusion MRI (dMRI) can introduce systematic errors in estimates of diffusion measures. While there are correction methods that can compensate for these errors, as presented in the Human Connectome Project, such nonlinear effects are often assumed to be negligible for typical applications, and as a result, gradient nonlinearities are mostly left uncorrected. In this work, we perform a systematic analysis to investigate the effect of gradient nonlinearities on dMRI studies, from voxel-wise estimates to group study outcomes. We present a novel framework to quantify and visualize these effects by decomposing them into their magnitude and angle components. Mean magnitude deviation and fractional gradient anisotropy are introduced to quantify the distortions in the size and shape of gradient vector distributions. By means of Monte-Carlo simulations and real data from the Human Connectome Project, the errors on dMRI measures derived from the diffusion tensor imaging and diffusional kurtosis imaging are highlighted. We perform a group study to showcase the alteration in the significance and effect size due to ignoring gradient nonlinearity correction. Our results indicate that the effect of gradient field nonlinearities on dMRI studies can be significant and may complicate the interpretation of the results and conclusions.
Subject(s)
Brain/diagnostic imaging , Diffusion Magnetic Resonance Imaging/methods , Neuroimaging/methods , Adult , Computer Simulation , Connectome , Diffusion Tensor Imaging/methods , Female , Humans , Male , Phantoms, Imaging , Research Design , Young AdultABSTRACT
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
ABSTRACT
Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.