Hey @olibclarke,
- The focus-masked region (F*V_k) is added to a volume where the focused region is zeroed out and density outside the focus mask mask from the consensus reconstruction is included ((1-F)*barV))
- This is then multiplied by the solvent mask to give the class volume.
Yep, this is correct!
What is the effect of replacing the voxels outside the focus mask with those from the consensus reconstruction, rather than just masking them out?
As @DanielAsarnow mentioned above, the effect is similar to particle/signal subtraction done ‘on the fly’ (with fixed poses). This is better than the single-mask approach because it accounts for the way in which the (1-F) region ‘bleeds into’ the (F) region due to occlusions, or the CTF.
More specifically, if you work through the residual math for this new mask formulation, you get two relevant terms, one of which is identical to the ‘single mask case’ (i.e., |I - PFV_k| where P contains the pose/shift/CTF) and one which is an inner product between PFV_k and P(1-F)barV. This latter term can change the class distribution.
And if this masking approach is beneficial for 3D classification, could/should it also be applied to 3D-VA?
3D-VA already accounts for this in its formulation since the components can only change the consensus density within the supplied mask, so we effectively have the same F*V_k + (1-F)*V_0 structure.
Hope that’s clear!