Data repository for the 2021 AAPM deep-learning sparse-view CT grand challenge

The training data for developing the neural networks for DL sparse-view CT are contained in this repository.
All files are compressed with gzip in order to facilate faster downloads.
Data are partitioned into four batches, which also facilates downloading of the
individual files. Data are in python numpy's .npy format.
After uncompressing with gunzip the .npy files can be read into python
with the numpy.load command, yielding single precision floating point arrays
of the proper dimensions.

Description of data files:
Phantom_batch?.npy
These arrays are 1000x512x512.
1000 images of pixel dimensions 512x512.
These are the true images.

FBP128_batch?.npy
These arrays are 1000x512x512.
1000 images of pixel dimensions 512x512.
These are the FBP reconstructed images from the 128-view sinograms.

Sinogram_batch?.npy
These arrays are 1000x128x1024.
1000 sinograms of 128 projections over 360 degree scanning onto a 1024-pixel linear detector. 

There are four batches. Thus 4000 sets of data/image pairs are available for training
the neural networks for image reconstruction.
The goal is to train a network that accepts the FBP128 image (and/or the 128-view sinogram)
to yield an image that is as close as possible to the corresponding Phantom image.

The python code metric_script.py was used to score the submissions to DL sparse-view CT.

Challenge report is published in Medical Phyiscs:

Sidky EY, Pan X. Report on the AAPM deep-learning sparse-view CT grand challenge. Med Phys. 2022; 49: 4935–4943.

See link below in Related Works.