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Recently, deep learning(DL) methods have been proposed for the low-dosecomputed tomography(LdCT) enhancement, and obtain good trade-off betweencomputational efficiency and image quality. Most of them need large number ofpre-collected ground-truth/high-dose sinograms with less noise, and train thenetwork in a supervised end-to-end manner. This may bring major limitations onthese methods because the number of such low-dose/high-dose training sinogrampairs would affect the network's capability and sometimes the ground-truthsinograms are hard to be obtained in large scale. Since large number oflow-dose ones are relatively easy to obtain, it should be critical to makethese sources play roles in network training in an unsupervised learningmanner. To address this issue, we propose an unsupervised DL method for LdCTenhancement that incorporates unlabeled LdCT sinograms directly into thenetwork training. The proposed method effectively considers the structurecharacteristics and noise distribution in the measured LdCT sinogram, and thenlearns the proper gradient of the LdCT sinogram in a pure unsupervised manner.Similar to the labeled ground-truth, the gradient information in an unlabeledLdCT sinogram can be used for sufficient network training. The experiments onthe patient data show effectiveness of the proposed method.

Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement