Report on the AAPM deep-learning sparse-view CT (DL-sparse-view CT) Grand Challenge

TL;DR

This paper reports on a challenge to develop deep-learning methods for sparse-view CT image reconstruction, demonstrating significant improvements in accuracy and exploring the potential of deep learning to solve inverse imaging problems.

Contribution

The paper introduces a large-scale challenge dataset and benchmarks for deep-learning-based sparse-view CT reconstruction, highlighting the effectiveness of advanced neural networks in inverse problems.

Findings

Winning team improved accuracy by two orders of magnitude.

Approximately 60 groups participated in validation, 25 in testing.

Deep learning shows promise in solving sparse-view CT inverse problems.

Abstract

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.