  Objective: This study aims at investigating a novel super resolution CBCT
imaging technique with the dual-layer flat panel detector (DL-FPD). Approach:
In DL-FPD based CBCT imaging, the low-energy and high-energy projections
acquired from the top and bottom detector layers contain intrinsically
mismatched spatial information, from which super resolution CBCT images can be
generated. To explain, a simple mathematical model is established according to
the signal formation procedure in DL-FPD. Next, a dedicated recurrent neural
network (RNN), named as suRi-Net, is designed by referring to the above imaging
model to retrieve the high resolution dual-energy information. Different
phantom experiments are conducted to validate the performance of this newly
developed super resolution CBCT imaging method. Main Results: Results show that
the proposed suRi-Net can retrieve high spatial resolution information
accurately from the low-energy and high-energy projections having lower spatial
resolution. Quantitatively, the spatial resolution of the reconstructed CBCT
images of the top and bottom detector layers is increased by about 45% and 54%,
respectively. Significance: In future, suRi-Net provides a new approach to
achieve high spatial resolution dual-energy imaging in DL-FPD based CBCT
systems.
