Kavli Affiliate: Feng Wang
| First 5 Authors: Feng Wang, Renfang Wang, Bo Yang, Hong Qiu,
| Summary:
Low-Dose computer tomography (LDCT) is an ideal alternative to reduce
radiation risk in clinical applications. Although
supervised-deep-learning-based reconstruction methods have demonstrated
superior performance compared to conventional model-driven reconstruction
algorithms, they require collecting massive pairs of low-dose and norm-dose CT
images for neural network training, which limits their practical application in
LDCT imaging. In this paper, we propose an unsupervised and training data-free
learning reconstruction method for LDCT imaging that avoids the requirement for
training data. The proposed method is a post-processing technique that aims to
enhance the initial low-quality reconstruction results, and it reconstructs the
high-quality images by neural work training that minimizes the $ell_1$-norm
distance between the CT measurements and their corresponding simulated sinogram
data, as well as the total variation (TV) value of the reconstructed image.
Moreover, the proposed method does not require to set the weights for both the
data fidelity term and the plenty term. Experimental results on the AAPM
challenge data and LoDoPab-CT data demonstrate that the proposed method is able
to effectively suppress the noise and preserve the tiny structures. And these
results also shows the proposed method’s low computational cost and rapid
convergence. The source code is available at
url{https://github.com/linfengyu77/IRLDCT}.
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