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The DL-sparse-view CT Challenge has concluded. The Challenge provided an opportunity for investigators in deep-learning CT image reconstruction to compete with their colleagues on the accuracy of their methodology for solving the inverse problem associated with sparse-view CT acquisition.

The top five performing individuals and teams are:

Username Team Name Members Institutions(s) RMSE Score
Max Robust-and-stable Martin Genzel
Jan Macdonald
Maximillian März
Utrecht University

Technical University of Berlin
6.37 x 10^(-6)
TUM YM & RH Youssef Mansour
Reinhard Heckel
Technical University of Munich

Rice University
3.99 x 10^(-5)
cebel67 DEEP_UL

Cédric Bélanger
Maxence Larose
Leonardo Di Schiavi Trotta
Rémy Bédard
Daniel Gourdeau

Université Laval 1.29 x 10^(-4)
deepx   Yading Yuan Icahn School of Medicine
at Mount Sinai
1.59 x 10^(-4)
Haimiao HBB Haimiao Zhang
Bin Dong
Baodong Liu
Beijing Information Science
and Technology University

Peking University

Chinese Academy of Sciences
1.81 x 10^(-4)

For more information see the Challenge website which will remain open: dl-sparse-view-ct-challenge.eastus.cloudapp.azure.com/competitions/1

The validation phase, which included about 60 active participants, and the final test phase included 25 submissions. Each submission consisted of an algorithm report along with predictions on 100 images from sparse view data. Final score was the mean root-mean-square-error (RMSE) calculated in comparison with the truth images. RMSE values for the top five appear in the table. For full ranking for validation and test phases, please see the ‘Results’ tab of the Challenge website.

A full Challenge report will be forthcoming at the end of August.