Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data.
Longitudinal and multi-site clinical studies create the imperative to characterize and correct technological sources of variance that limit image reproducibility in high-resolution structural MRI studies, thus facilitating precise, quantitative, platform-independent, multi-site evaluation. In this work, we investigated the effects that imaging gradient non-linearity have on reproducibility of multi-site human MRI. We applied an image distortion correction method based on spherical harmonics description of the gradients and verified the accuracy of the method using phantom data. The correction method was then applied to the brain image data from a group of subjects scanned twice at multiple sites having different 1.5 T platforms. Within-site and across-site variability of the image data was assessed by evaluating voxel-based image intensity reproducibility. The image intensity reproducibility of the human brain data was significantly improved with distortion correction, suggesting that this method may offer improved reproducibility in morphometry studies. We provide the source code for the gradient distortion algorithm together with the phantom data.
Jovicich
Jorge
J
MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Building 149, 13th Street, Radiology/CNY149-Room 2301, Charlestown, MA 02129, USA. jovicich@nmr.mgh.harvard.edu
Czanner
Silvester
S
Greve
Douglas
D
Haley
Elizabeth
E
van der Kouwe
Andre
A
Gollub
Randy
R
Kennedy
David
D
Schmitt
Franz
F
Brown
Gregory
G
Macfall
James
J
Fischl
Bruce
B
Dale
Anders
A
D002138:Calibration; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015337:Multicenter Studies as Topic / Q000379:methods; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results