CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction
H. Gupta, K.H. Jin, H.Q. Nguyen, M.T. McCann, M. Unser
IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1440–1453, June 2018.
We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism to enforce that the reconstructed image is consistent with the measurements. We propose a relaxed version of PGD wherein gradient descent enforces measurement consistency, while a CNN recursively projects the solution closer to the space of desired reconstruction images. We show that this algorithm is guaranteed to converge and, under certain conditions, converges to a local minimum of a non-convex inverse problem. Finally, we propose a simple scheme to train the CNN to act like a projector. Our experiments on sparse-view computed tomography reconstruction show an improvement over total variation-based regularization, dictionary learning, and a state-of-the-art deep learning-based direct reconstruction technique.
@ARTICLE(http://bigwww.epfl.ch/publications/gupta1802.html,
AUTHOR="Gupta, H. and Jin, K.H. and Nguyen, H.Q. and McCann, M.T. and
Unser, M.",
TITLE="{CNN}-Based Projected Gradient Descent for Consistent {CT} Image
Reconstruction",
JOURNAL="{IEEE} Transactions on Medical Imaging",
YEAR="2018",
volume="37",
number="6",
pages="1440--1453",
month="June",
note="")