Normalization using Gate simulation data

Hi everyone,

I am tring to recon my own scanner data by pytomography. It looks like very well. But when I wanner check normalization, it looks terrible.

At first, I would like to do normalization by Gate simulation without phantom(while source is backtoback), which means the output data only contain true and random coincidence, so I cannot attenuation. Only use true coincidence to do normalization and recon, it looks good, as below.

Then, I wanner try Gate simulation with phantom(ring from 97 to 100 mm with Polyethylene, and cylinder from 0 to 97 mm with water, while source is F18), which means the output data contain attenuation, random and scatter(like real world). I wanner use true coincidence data to do normalization and recon. But lt looks terrible, as below.

dose It mean I need to do attenuation correction before normalization?

Beside, I’m also learning the function of ‘gate.get_normalization_weights_cylinder_calibration’. It looks like component based normalization, but is different with the fomular(from the paper of ‘Normalization of Monte Carlo PET data using GATE’).
image
I lost in it, could you give any fomular in any reference?

Thanks for any reply.

Best,
Hannah

Hi Hannah,

The function gate.get_normalization_weights_cylinder_calibration can only be used to derive normalization weights in the case where you acquire data from a thin cylindrical phantom (shell) with radius approaching the edge of the scanner. I don’t think you can use the true coincidence data from your phantom to derive the normalization coefficients.

Let me know if this helps!

Hi Luke,

Got it. You mean only ring with scanner radius can compute normalization weights. I will try again!

Thanks a lot.

Cheers,
Hannah

It works!

Next, I will try to normalize small animal PET with cross dual-layer crystal.

Cheers,
Hannah

Hi Luke,

After I changed norm phantom from cylinder to ring (99/100 mm smaller than scanner radius 102 mm), I found there still is artifact around the transverse cylinder(raduis 85 mm), as blow. Besides, It seems same profile between norm and norm_ac.

What’s more, It confused me about the histo, why not [Nr_crystal_axial_bins, Nr_crystal_axial_bins, Nr_crystal_trans_bins, Nr_crystal_trans_bins, Nr_delta_submodule_axial_bins, Nr_delta_submodule_trans_bins, Nr_delta_module_axial_bins, Nr_delta_module_trans_bins, Nr_delta_rsector_trans_bins]. Although I got the same artifact in recon image after adding Nr_delta_submodule_trans_bins and Nr_delta_module_trans_bins.

Lastly, dose it contain the sensitivity between different detectors and crystals? What if I wanner do normalization with real PET system data?

Thanks for any reply.

Best,
Hannah

dear Hannah,
please i am working on CT scan simulation using GATE, i got projection data output in this format ( benchmarkCT_000.dat … benchmarkCT_089.dat), i stuck all of this projections in a sinogram matrix (in matlab), i need help how can i do reconstructions using pyTomography?
thank you for your help
best regards

Dear Hassan,

Sorry, I have not recon CT images by Tomography yet. I just tried it by CASToR. It’s also easy to use.

Best,
Hannah

dear Hannah,
thank you for your reply, could you please verify my GATE CT scan code with me , i am not sure about it, i obtained this image of chest using a custom code python reconstruction, this is my email: hassan.ouhadda@ump.ac.ma
regards
CTchest0