  In this work, we present a novel self-supervised method for Low Dose Computed
Tomography (LDCT) reconstruction. Reducing the radiation dose to patients
during a CT scan is a crucial challenge since the quality of the reconstruction
highly degrades because of low photons or limited measurements. Supervised deep
learning methods have shown the ability to remove noise in images but require
accurate ground truth which can be obtained only by performing additional
high-radiation CT scans. Therefore, we propose a novel self-supervised
framework for LDCT, in which ground truth is not required for training the
convolutional neural network (CNN). Based on the Noise2Inverse (N2I) method, we
enforce in the training loss the equivariant property of rotation
transformation, which is induced by the CT imaging system, to improve the
quality of the CT image in a lower dose. Numerical and experimental results
show that the reconstruction accuracy of N2I with sparse views is degrading
while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps
better image quality over a different range of sampling angles. Finally, the
quantitative results demonstrate that RAN2I achieves higher image quality
compared to N2I, and experimental results of RAN2I on real projection data show
comparable performance to supervised learning.
