Skip to content

CAAI/PE2I_denoiser

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 

Repository files navigation

PE2I_denoiser

The tool denoises Vision and Quadra PET data acquired with PE2I tracer at low-doses.

The model was trained with ~100 PE2I patients imaged on a Siemens Vision mCT system for 10 minutes with >180 MBq injected dose. Low-dose was simulated at a 5% level.

The model is transfer learned from the model proposed by Raphael S. Daveau, et al. Deep learning based low-activity PET reconstruction of [18F]FE-PE2I and [11C]PiB in Neurodegenerative Disorders, NeuroImage (2022).

Usage

Use with original data

You can use the model on your own data without any preprocessing: PE2I_denoiser --pet <PATH_TO_PET.nii.gz> --ct <PATH_TO_CT.nii.gz> --out <OUTPUT_DENOISED_PET.nii.gz>

You can also supply a path to a directory with DICOM files as input (both PET and CT). OBS: Output continues to be in NII.GZ for now.

If you wish to scale the input data (e.g. for decay correction) use the --scale flag that scaled the low-dose input.

You can save the intermediate files by giving the flags -si -io <OUTPUT_DIR>

The expected runtime for the model including pre/post-processing is ~10 minutes.

Use with preprocessed data

If you do not supply the --ct flag, no pre/post-processing will be performed, i.e. the model assumes that your data is:

  • skullstripped,
  • spatially normalized to MNI space (256x256x256 matrix / 1mm isotropic voxel size),
  • with images cropped to 176x176x200,
  • decay correction and 95% percentile normalization applied

If so, only inference will be performed, which takes ~20 seconds.