  Cone Beam Computed Tomography(CBCT) is a now known method to conduct CT
imaging. Especially, The Low Dose CT imaging is one of possible options to
protect organs of patients when conducting CT imaging. Therefore Low Dose CT
imaging can be an alternative instead of Standard dose CT imaging. However Low
Dose CT imaging has a fundamental issue with noises within results compared to
Standard Dose CT imaging. Currently, there are lots of attempts to erase the
noises. Most of methods with artificial intelligence have many parameters and
unexplained layers or a kind of black-box methods. Therefore, our research has
purposes related to these issues. Our approach has less parameters than usual
methods by having Iterative learn-able bilateral filtering approach with Deep
reinforcement learning. And we applied The Iterative learn-able filtering
approach with deep reinforcement learning to sinograms and reconstructed volume
domains. The method and the results of the method can be much more explainable
than The other black box AI approaches. And we applied the method to Helical
Cone Beam Computed Tomography(CBCT), which is the recent CBCT trend. We tested
this method with on 2 abdominal scans(L004, L014) from Mayo Clinic TCIA
dataset. The results and the performances of our approach overtake the results
of the other previous methods.
