Ok, sorting the images in to separate folders would be a good start. If you pre-sort into separate folders before loading, it should be possible to get rid of the jumbled single volume. If you are able to load and split in ImageJ, you might be able to save as NRRD using e.g. BioFormats or one of the other plugins with NRRD support (but those probably don’t know about DWI metadata).
If ImageJ doesn’t provide folder sorting, then other options include this and this (same name, both Python, but different projects).
As for actually interpreting the data as diffusion, you will need a way to extract the gradient information. Some comments:
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I don’t know what “Bruker diffusion DICOM” looks like. If you are really lucky, then Bruker is using the standard diffusion tags, and DWIConvert may just work – once you sort the files appropriately. (sample data would be very helpful if you are able to provide any. we got some sample data from someone else in June, but it did not appear to have any DWI metadata).
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The most popular NiFTI DWI conversion tool, dcm2nii, can apparently read Bruker 2dseq style files with diffusion information, but I don’t know if it supports Bruker DICOM (again dependent on the tags).
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DSI-Studio apparently also has some Bruker support (2dseq again?) but my vague recollection is that their interpretation of NiFTI DWI differs a little bit from the “FSL NiFTI DWI” that dcm2nii and compatible tools create/expect.
Hopefully something here is helpful to get started. If you do have shareable sample data I can take a quick look and maybe point you further along.