listmode DL notebooks for PSMR 2024

[gschramm]

Don't merge yet, because:

• first needs merging of SIRF PR Computing Hessian products with image data objects and related stuff SIRF#1253
• clean-up files + final conversion of ??_*.py notebooks to jupyter notebook

[gschramm]

@KrisThielemans for adding the LM caching:

• here is where I setup the ListModeData object
• here is where I setup the listmode logL and reconstruct

[KrisThielemans]

Some quick comments:

• Is your random prefix time-frame dependent? They will need to have different scale, as well as ExamInfo. (We could come up with a function to create one, and then scale it down, but it's going to be ugly, and we have no time.)
• can speed-up things with set_max_segment_num_to_process in the reconstructor (possibly also in ListmodeToSinograms)
• https://github.com/gschramm/SIRF-Exercises/blob/2cdda25975855a4e4c96be8f5507b2712c73e2df/notebooks/Deep_Learning_listmode_PET/01_SIRF_listmode_recon.py#L199C1-L199C44 is wrong comment
• try lm_obj_fun.set_max_cache_size(large_number). probably need to set cache_path for reliable operation

[gschramm]

1. Isn't it the easiest / most efficient if we only unlist + reconstruct the complete (1m and 60min) list mode files. Should avoid all kind of problems ...
2. is the "large number" in bytes / MB / GB?
3. Just starting reconstructing the 60min data after downloading. But I immediately get this error when reading the mumap file:

attn_image = sirf.STIR.ImageData("../../data/PET/mMR/NEMA_IQ/20170809_NEMA_MUMAP_UCL.v.hdr")

error: ??? '\'\\nERROR: Error parsing: expected a vectorised key as in "image duration (sec)[1]", but no bracket found\\n\' 
exception caught at line 417 of /users/nexuz/gschra2/nge/SIRF/build_lm/sources/SIRF/src/xSTIR/cSTIR/cstir.cpp; the reconstruction engine output may provide more information'

The header looks like this:

!IMAGE DATA DESCRIPTION:=
!total number of data sets:=1
!image duration (sec):=0
!image relative start time (sec):=0

[KrisThielemans]

1. sure
2. bytes
3. yes, the full notebook contains extra stuff to handle this, but easiest is to use the same attenuation image and norm (or indeed acq_sens_model) as for the 1 min data. Note that there will be scale factor of 60 (or nearly, due to decay) between the images, as that's currently not yet taken into account.

[gschramm]

Thanks Kris! Will update the notebooks accoordingly tmr.
To take care of the scale difference for the 60min ref. image, I will probably just rescale based on the mean intensity.

[KrisThielemans]

I'll merge this in a few minutes

[gschramm]

@KrisThielemans ready to be merged

[KrisThielemans]

squash or not? As you prefer

[gschramm]

please squash, but let me fix one typo before

[gschramm]

@KrisThielemans : typos fixed, ready to be squashed and merged