  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: Using sparse-view data from images either in the training or
testing set, there is a significant discrepancy between the image obtained with
the CNN and the image that generated the data. For the same simulated scanning
conditions, TVmin is able to accurately reconstruct the test image. 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. Furthermore, this negative result is obtained for
conditions where TVmin is able to recover the test images. 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.
