  This Special Report summarizes the 2022 AAPM Grand Challenge on Deep-Learning
spectral Computed Tomography (DL-spectral CT) image reconstruction. The purpose
of the challenge is to develop the most accurate image reconstruction algorithm
for solving the inverse problem associated with a fast kVp switching
dual-energy CT scan using a three tissue-map decomposition. Participants could
choose to use deep-learning (DL), iterative, or a hybrid approach. Test phase
submission were received from 18 research groups. Both the winning and second
place teams had highly accurate results where the RMSE was nearly zero to
single floating point precision. Results from the top ten also achieved a high
degree of accuracy; and as a result this special report outlines the
methodology developed by each of these groups. The DL-spectral CT challenge
successfully established a forum for developing image reconstruction algorithms
based on deep-learning that address an important inverse problem relevant for
spectral CT.
