Do CNNs Solve the CT Inverse Problem?

OBJECTIVE This work examines the claim made in the literature that the inverse problem associ- ated 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 gener- ated 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 prob- lem 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 to solve inverse problems in medical imaging.