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

Abstract

Diffusion-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 combination with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. DW-SSFP datasets were acquired from five fixed whole human postmortem 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.

The Digital Brain Bank, an open access platform for post-mortem datasets

“…Often, post-mortem investigations are limited to small sections of excised brain tissue that represent a limited anatomical region. However, our developments in whole-brain post-mortem diffusion imaging (Foxley et al, 2014; McNab et al, 2009; Miller et al, 2011, 2012; Tendler, Foxley, Hernandez-Fernandez, et al, 2020) provide the opportunity to investigate structural connectivity and gross neuroanatomy, at scales that are unobtainable in vivo. These developments have culminated in the Human High-Resolution Diffusion MRI-PLI dataset, providing one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired (500 μm isotropic resolution), as shown in Fig.…”

“…Despite this potential, post-mortem MRI remains a relatively niche approach, in part due to technical challenges and need for multi-disciplinary expertise. In order to provide post-mortem MRI as an experimental technique to neuroscientists in Oxford, we have had to develop a broad range of underpinning technologies, including: (i) pulse sequences that provide high quality data under the harsh imaging conditions of postmortem tissue (McNab et al, 2009; Miller et al, 2011); (ii) analyses that account for the signal formation mechanisms of these sequences (Tendler, Foxley, Cottaar, et al, 2020) or properties unique to post-mortem tissue (Tendler, Qi, et al, 2020); (iii) experimental approaches that enable the use of ultra-high field MRI to increase SNR for high-resolution imaging (Foxley et al, 2014; Tendler, Foxley, Hernandez-Fernandez, et al, 2020); (iv) development of custom sample holders to maximise SNR and minimise imaging artefacts (Supporting Information Figs. S1 and S2); (v) tools for aligning small 2D microscopy images into 3D whole-brain MRI (Huszar et al, 2019); (vi) strategies for co-analysing MRI and microscopy data (A. F. Howard et al, 2019; Mollink et al, 2017) and (vii) techniques for between-species comparisons (Eichert et al, 2020; Mars et al, 2018).…”

“…To facilitate cross-dataset comparisons, the majority of diffusion datasets from the Digital Brain Bank provide derived diffusivity estimates in the form of diffusion tensor and/or ball & sticks model parameters (Behrens et al, 2007). Whilst there are a number of standard software packages available for DW-SE data, this was achieved for DW-SSFP datasets using a custom imaging pipelines incorporating the full DW-SSFP model (including T 1 , T 2 and B 1 dependencies) (Tendler, Foxley, Hernandez-Fernandez, et al, 2020).…”

Modeling an equivalent b‐value in diffusion‐weighted steady‐state free precession

“…This confound prevents a simple interpretation of results between, or even within DW‐SSFP datasets. One approach is to use the model parameters to derive an ADC map with the same effective b‐value within every voxel regardless of local , representing a common snapshot of restricted diffusion . This approach could additionally account for the variations in , , and the diffusivity of tissue, which will also influence the effective b‐value (see Supporting Information Figure ).…”

A method to remove the influence of fixative concentration on postmortem T2 maps using a kinetic tensor model

“…Both the concentration maps and tissue masks were estimated in the diffusion space of the post-mortem brains, and transformed to the space of the T 2 maps using FSL FLIRT (Jenkinson & Smith, 2001; 6 degrees of freedom, estimated from the unprocessed TSE and DW-SSFP b0 data). A 6 degrees of freedom transformation was sufficient as the acquisition bandwidth of the diffusion scans (393 Hz/Pixel; Tendler et al, 2020) was similar to the TSE scans (166 Hz/Pixel).…”