Open AccessAngular versus spatial resolution trade‐offs for diffusion imaging under time constraints
Author(s) -
Zhan Liang,
Jahanshad Neda,
Ennis Daniel B.,
Jin Yan,
Bernstein Matthew A.,
Borowski Bret J.,
Jack Clifford R.,
Toga Arthur W.,
Leow Alex D.,
Thompson Paul M.
Publication year - 2013
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22094
Subject(s) - diffusion mri , voxel , fractional anisotropy , angular resolution (graph drawing) , isotropy , kurtosis , anisotropy , diffusion , image resolution , orientation (vector space) , stability (learning theory) , nuclear magnetic resonance , imaging phantom , signal (programming language) , signal to noise ratio (imaging) , physics , magnetic resonance imaging , computer science , mathematics , optics , artificial intelligence , statistics , geometry , medicine , combinatorics , machine learning , radiology , programming language , thermodynamics
Diffusion weighted magnetic resonance imaging (DW‐MRI) are now widely used to assess brain integrity in clinical populations. The growing interest in mapping brain connectivity has made it vital to consider what scanning parameters affect the accuracy, stability, and signal‐to‐noise of diffusion measures. Trade‐offs between scan parameters can only be optimized if their effects on various commonly‐derived measures are better understood. To explore angular versus spatial resolution trade‐offs in standard tensor‐derived measures, and in measures that use the full angular information in diffusion signal, we scanned eight subjects twice, 2 weeks apart, using three protocols that took the same amount of time (7 min). Scans with 3.0, 2.7, 2.5 mm isotropic voxels were collected using 48, 41, and 37 diffusion‐sensitized gradients to equalize scan times. A specially designed DTI phantom was also scanned with the same protocols, and different b ‐values. We assessed how several diffusion measures including fractional anisotropy (FA), mean diffusivity (MD), and the full 3D orientation distribution function (ODF) depended on the spatial/angular resolution and the SNR. We also created maps of stability over time in the FA, MD, ODF, skeleton FA of 14 TBSS‐derived ROIs, and an information uncertainty index derived from the tensor distribution function, which models the signal using a continuous mixture of tensors. In scans of the same duration, higher angular resolution and larger voxels boosted SNR and improved stability over time. The increased partial voluming in large voxels also led to bias in estimating FA, but this was partially addressed by using “beyond‐tensor” models of diffusion. Hum Brain Mapp 34:2688–2706, 2013. © 2012 Wiley Periodicals, Inc.