I have read the “Fibre density and cross-section - Single shell DWI” manual. It said that high b-value (>2000s/mm2) data is recommended to aid the interpretation of AFD being related to the intra-axonal space.
While my DWI data just have one b value, 1000s/mm2. I wonder if I can use fixel-based analysis to my data?
Any differences in FD or FDC will be difficult to interpret: unlike with high b-value data, it is not only the intra-axonal space that will have contributed to your DWI signal, and hence the FOD amplitudes will be confounded by e.g. diffusivity / tortuosity of the extra-cellular space. You will also struggle to resolve some crossing fibre regions, both in individual subjects and in the FOD template. However this does not totally preclude you from performing FBA using your data, and indeed you may still be able to achieve some fibre specificity & sensitivity; it’s primarily the capability to interpret any changes observed that will be lacking.
Apart from the capability to resolve crossing fibres in the definition of fixels, the FC metric should not be influenced by the lower b-value: It is a morphological metric based on registration to the template, and hence the specific sensitivity to intra-axonal space is less relevant.
How does this relate to post-mortem data (in rats)? We have acquired a post-mortem dataset at 9.4T, 150 micron isotropic voxels, a b-value of 3850 and 60 directions. In this case, will potential differences between FD and FDC be interpretable? Or do we need much higher b-values, since we are analysing post-mortem tissue?
I’m not an expert on ex vivo imaging, but my understanding is that the rule of thumb is for ex vivob-values to be 3-4 times higher than in vivo to obtain comparable angular contrast. So your data are probably comparable to “DTI” b-values of ~ 1000-1200. I don’t know whether or not fibre morphology changes substantially during the fixation process, which could influence the relative volumes & restriction geometry of intra-cellular vs. extra-cellular water. But in general I would suggest that the comments made above are fairly equally applicable to your data.