Implementing highly efficient internal volume codec that is targeting the volumes upsampled using the NN-interpolation.  This is a critical function during the PET-4D to MR registration.
User optimally desires best available resolution for both registered volume, which is typically a MR resolution.  However, for high timepoint # the resulting volumes might reach upto 7 GB  (compressed with the regular FireVoxel compression).
This new codec will allow much higher compression of such registered PET volumes.

It is lossless.

High-accuracy PET-to-MR registration using an approximate BrainMask with 4X speed gain.

Presently, it is very hard to achieve PET-to-MR registration without additional guidance.
From previous registration experience, a quick  Brain ROI was obtained using the BrainMask\EdgeWave tool.

This ROI is just an approximation of an anatomically precise mask of the brain. FireVoxel can obtained a much more precise BrainMask by adding the non-uniformity correction to that workflow.



After obtaining BrainMask ROI, the regular  PET-to-MR registration runs that uses ROI as a guidance.  Resulting registration has a very high accuracy.
The whole processing is less than 4 minutes on the reference ($1K) PC.

Single volume Edge-constrained smoothing:  partially combined the code with the similar logic of "Inter-volume constrained smoothing".  Reused the dialog common for both functions.

Corrected the logic for the Registration3D: TargetROI.  Included a new  dialog item that explicitly displays the name of the layer to be used as a Target ROI.

Implemented an initial variant of the "Inter-volume Edge Constrained Smoothing".  In this  test:
a) MR and PET4D were registered with very high accuracy.
b) MR-edges (as 3D surfaces) were detected using the texture-edge detector (URAL)
c) PET-volume was smoothed, with smoothing apperture that was constrained by the presence
of edges within vicinity of every voxel.

This just a prototype as many options are available.
Specialized dialog box was developed.

Implemented the two-compartment Sokoloff'77 model (long formula) also used in Reivich'85. Results are identical (to 0.01% precision) with the Sokoloff'79 interpretation (short formula)
in the initial tests.