Do CNNs Solve the CT Inverse Problem?

Objective: This work examines the claim made in the literature that the inverse problem associated with image reconstruction in sparse-view computed tomography (CT) can be solved with a convolutional neural network (CNN). Methods: Training, and testing image/data pairs are generated in a dedicated breast CT simulation for sparse-view sampling, using two different object models. The trained CNN is tested to see if images can be accurately recovered from their corresponding sparse-view data. For reference, the same sparse-view CT data is reconstructed by the use of constrained total-variation (TV) minimization (TVmin), which exploits sparsity in the gradient magnitude image (GMI). Results: There is a significant discrepancy between the image obtained with the CNN and the image that generated the data. TVmin is able to accurately reconstruct the test images. Conclusion: We find that the sparse-view CT inverse problem cannot be solved for the particular published CNN-based methodology that we chose, and the particular object model that we tested. Significance: The inability of the CNN to solve the inverse problem associated with sparse-view CT, for the specific conditions of the presented simulation, draws into question similar unsupported claims being made for the use of CNNs and deep-learning to solve inverse problems in medical imaging.

Automated summary

This study investigates the use of CNN for solving the inverse problem of image reconstruction in sparse-view CT, finding that CNN is unable to accurately recover images in this context but constrained total-variation minimization can. This raises doubts on similar unsupported claims regarding the use of CNNs and deep-learning for solving inverse problems in medical imaging.