  Purpose: The purpose of the challenge is to find the deep-learning technique
for sparse-view CT image reconstruction that can yield the minimum RMSE under
ideal conditions, thereby addressing the question of whether or not deep
learning can solve inverse problems in imaging. Methods: The challenge set-up
involves a 2D breast CT simulation, where the simulated breast phantom has
random fibro-glandular structure and high-contrast specks. The phantom allows
for arbitrarily large training sets to be generated with perfectly known truth.
The training set consists of 4000 cases where each case consists of the truth
image, 128-view sinogram data, and the corresponding 128-view filtered
back-projection (FBP) image. The networks are trained to predict the truth
image from either the sinogram or FBP data. Geometry information is not
provided. The participating algorithms are tested on a data set consisting of
100 new cases. Results: About 60 groups participated in the challenge at the
validation phase, and 25 groups submitted test-phase results along with reports
on their deep-learning methodology. The winning team improved reconstruction
accuracy by two orders of magnitude over our previous CNN-based study on a
similar test problem. Conclusions: The DL-sparse-view challenge provides a
unique opportunity to examine the state-of-the-art in deep-learning techniques
for solving the sparse-view CT inverse problem.
