Emil Y Sidky1, Xiaochuan Pan1. 1. Department of Radiology, University of Chicago, Chicago, Illinois, USA.
Abstract
PURPOSE: The purpose of the challenge is to find the deep-learning (DL) technique for sparse-view computed tomography (CT) image reconstruction that can yield the minimum root mean square error (RMSE) under ideal conditions, thereby addressing the question of whether or not DL can solve inverse problems in imaging. METHODS: The challenge setup 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 DL methodology. The winning team improved reconstruction accuracy by two orders of magnitude over our previous convolutional neural network (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 DL techniques for solving the sparse-view CT inverse problem.
PURPOSE: The purpose of the challenge is to find the deep-learning (DL) technique for sparse-view computed tomography (CT) image reconstruction that can yield the minimum root mean square error (RMSE) under ideal conditions, thereby addressing the question of whether or not DL can solve inverse problems in imaging. METHODS: The challenge setup 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 DL methodology. The winning team improved reconstruction accuracy by two orders of magnitude over our previous convolutional neural network (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 DL techniques for solving the sparse-view CT inverse problem.