Mercure module to deploy pyapetnet - a convolutional neural network (CNN) to mimick the behavior of anatomy-guided PET reconstruction in image space. This module runs as a docker container in mercure, it can be added to an existing mercure installation using docker tag : mercureimaging/mercure-pyapetnet. The module will run with the pyapetnet S2_osem_b10_fdg_pe2i trained model by default.
Follow instructions on mercure website on how to add a new module. Use the docker tag mercureimaging/mercure-pyapetnet.
- Clone repo.
- Build Docker container locally by running make (modify makefile with new docker tag as needed).
- Test container :
docker run -it -v /input_data:/input -v /output_data:/output --env MERCURE_IN_DIR=/input --env MERCURE_OUT_DIR=/output mercureimaging/mercure-pyapetnet
The mercure-pyapetnet module requires that mercure provides a single MRI series and a single PET series for processing. Therefore, it is important that the processing rules are configured to filter out any surplus data received by mercure, an example is shown in the screenshot below where the rule will select series containing 'MPRAGE' or 'PET_AC' in the series description, and processing will be triggered when both series have been received. More information on mercure rule configuration can be found here.
By default the module will select the pyapetnet S2_osem_b10_fdg_pe2i trained model. Other models can be selected easily in the processing module 'Settings' tab as shown in the screenshot below. The series description of the output DICOM files can also be set. Current settings available with default values are listed below.
"trained_model": "S2_osem_b10_fdg_pe2i"
"series_desc": "Bowsher_"
"series_suffix":"DEFAULT"
