  Cone Beam Computed Tomography (CBCT) is widely used in dentistry for
diagnostics and treatment planning. CBCT Imaging has a long acquisition time
and consequently, the patient is likely to move. This motion causes significant
artifacts in the reconstructed data which may lead to misdiagnosis. Existing
motion correction algorithms only address this issue partially, struggling with
inconsistencies due to truncation, accuracy, and execution speed. On the other
hand, a short-scan reconstruction using a subset of motion-free projections
with appropriate weighting methods can have a sufficient clinical image quality
for most diagnostic purposes. Therefore, a framework is used in this study to
extract the motion-free part of the scanned projections with which a clean
short-scan volume can be reconstructed without using correction algorithms.
Motion artifacts are detected using deep learning with a slice-based prediction
scheme followed by volume averaging to get the final result. A realistic motion
simulation strategy and data augmentation has been implemented to address data
scarcity. The framework has been validated by testing it with real
motion-affected data while the model was trained only with simulated motion
data. This shows the feasibility to apply the proposed framework to a broad
variety of motion cases for further research.
