Use of multi-flip angle measurements to account for transmit inhomogeneity and non-Gaussian diffusion in DW-SSFP [article]

Benjamin C. Tendler, Sean Foxley, Moises Hernandez-Fernandez, Michiel Cottaar, Connor Scott, Olaf Ansorge, Karla Miller, Saad Jbabdi
<span title="2019-12-02">2019</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
AbstractDiffusion-weighted steady-state free precession (DW-SSFP) is an SNR-efficient diffusion imaging method. The improved SNR and resolution available at ultra-high field has motivated its use at 7T. However, these data tend to have severe B1 inhomogeneity, leading not only to spatially varying SNR, but also to spatially varying diffusivity estimates, confounding comparisons both between and within datasets. This study proposes the acquisition of DW-SSFP data at two-flip angles in
more &raquo; ... with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. DW-SSFP datasets were acquired from five fixed whole human post-mortem brains with a pair of flip angles that jointly optimize the diffusion contrast-to-noise across the brain. We compared one and two flip-angle DW-SSFP data using a diffusion tensor model that incorporates the full DW-SSFP Buxton signal model. The two-flip angle data were subsequently fitted using a modified DW-SSFP signal model that incorporates a Gamma distribution of diffusivities. This allowed us to generate tensor maps at a single, SNR-optimal effective b-value yielding more consistent SNR across tissue, in addition to eliminating the B1 dependence on diffusion coefficients and orientation maps. Our proposed approach will allow the use of DW-SSFP at 7T to derive diffusivity estimates that have greater interpretability, both within a single dataset and between experiments.HighlightsB1 inhomogeneity at 7T leads to spatially varying SNR & ADC estimates in DW-SSFP2-flip angle DW-SSFP data can address B1 effects in a cohort of post-mortem brainsOur approach reduces degradations in PDD estimates & improves whole brain coverageOur approach provides a means to define ADCs at an SNR-optimal effective b-value
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