I've collected some FIB-SEM tomography datasets for characterization of corrosion of Molten Salt Reactor materials. I'm just now coming to understand how difficult data fusion from multiple channels can really be, and was wondering if what I'm trying to do would be feasible for larger numbers of samples.
Problem 1) Analysis of RGB EDS/EBSD tif stacks
Whenever an operation (alignment, rescaling, etc.) is performed on stacks of RGB - EDS/EBSD images, the resultant images are output in greyscale only. For EDS maps, of individual elements, my workaround is to apply false color to each element stack in the recipe. However, I am mostly colorblind, and probably introduce a lot of inconsistencies with this method. It is also very manually intensive.
The main concern is when I try to align the Composite EDS maps of all the overlaid elements (Generated by our EDS collection software, not MIPAR). Once the color information is lost from the composite map, I cannot trace corrosion products through the grain boundaries.
Problem 2) Rescaling Images - Multiple Channels
I cannot perform stack alignments on image stacks of differing sizes. The Backscatter images collected by the microscope are 1536x1024 in size, whereas the EDS software collects 512x400 resolution images of both Secondary Electron and EDS/EBSD signals. I cannot fix this during data acquisition because neither microscope, nor EDS software share a mutual resolution (I can get them close but the image sizes are always off by a few hundred pixels).
I can either upscale the low resolution EDS maps, or downscale the High resolution BSE images. However, that seems to introduces scaling errors in feature sizes that will make alignments between datasets challenging. It will also introduce errors in any quantitative measurements of segmented features.
The goal is to create a repeatable workflow I can use to study dozens of tomography datasets for molten salt corrosion in the future. The workflow can be manually intensive - I'm not necessarily looking for a single recipe to handle all of the 3D reconstruction. I'm just wondering if there's a general process I can follow.
Are there any relatively simple techniques in the Fib-Tomography world to overcome challenges 1 & 2? Or would each dataset, require a highly specialized, and labor intensive (and error prone) workflow to deliver fused datasets.