title: Angular versus spatial resolution trade-offs for diffusion imaging under time constraints.
abstract: 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.
authors.affiliation: Department of Neurology, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, California.
authors.forename: Liang
authors.initials: L
authors.lastname: Zhan
authors.forename: Neda
authors.initials: N
authors.lastname: Jahanshad
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Ennis
authors.forename: Yan
authors.initials: Y
authors.lastname: Jin
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Bernstein
authors.forename: Bret J
authors.initials: BJ
authors.lastname: Borowski
authors.forename: Clifford R
authors.initials: CR
authors.lastname: Jack
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
authors.forename: Alex D
authors.initials: AD
authors.lastname: Leow
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001931:Brain Mapping / Q000379:methods* / Q000706:statistics & numerical data; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D017352:Echo-Planar Imaging / Q000379:methods; D005260:Female; D019416:Head Movements; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D009412:Nerve Fibers / Q000502:physiology; D019047:Phantoms, Imaging; D012016:Reference Values; D015203:Reproducibility of Results; D012107:Research Design; D059629:Signal-To-Noise Ratio; D013997:Time Factors