DeepAC is a deep learning network for obtaining attenuation correction umaps. The network currently works with input images:
- Dixon-VIBE (VE11P)
- UTE (VE11P)
Future version will also include T1w-MPRAGE and VB20P versions of all three. If you are in need already now, please contact firstname.lastname@example.org
At some point, a more smooth sharing will be implemented, but for now, please just download the main python scripts and keras models.
First, if you need to use GPU, you need to install the GPU, CUDA and cuDNN toolkits. See https://www.tensorflow.org/install/gpu
Then, download the DeepAC files:
DeepDixon script (version August 20 2019)
DeepUTE script (version March_12_2019)
Models for DeepUTE and DeepDixon (1 GB)
Requirements file for installation
To install the required software tools, run:
pip3 install -r requirements.txt
Please note - to use DeepDixon, you further need to install FSL.
The best performance will be obtained with GPU support (about 4 seconds, depending on GPU type), but CPU can also be used. Please install tensorflow rather than tensorflow-gpu (in requirements). The running time is about 15-20 minutes for CPU.
Running the script
Change the root folder in the main script (on line 23) to where you put the main script.
Run: python3 process_DeepUTE.py ﹤path to DICOM data﹥
Optionally, you can supply different models or change the output directory. See within top of file for explanations.
The output will be a folder called DeepUTE within the DICOM data folder.
Claes Ladefoged, Rigshospitalet, Copenhagen, Denmark
The publication has been submitted for review - please contact email@example.com for details on citations in the mean time