Super resolution dual-layer CBCT imaging with model-guided deep learning.

This study aims at investigating a novel super resolution CBCT imaging approach with dual-layer flat panel detector (DL-FPD).
Approach: With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampled spatial information, from which super resolution CT images can be reconstructed. A simple mathematical model is proposed to explain the signal formation procedure in DL-FPD, and a dedicated recurrent neural network (RNN), named as suRi-Net, is developed based upon the above imaging model to nonlinearly retrieve the high resolution dual-energy information. Physical benchtop experiments are conducted to validate the performance of this newly developed super resolution CBCT imaging method.
Main Results: Results demonstrate that the proposed suRi-Net can accurately retrieve high spatial resolution information from the low-energy and high-energy projections of low spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images from the top and bottom detector layers is increased by about 45% and 54%, respectively.
Significance: In the future, suRi-Net provides a new approach to perform high spatial resolution dual-energy imaging in DL-FPD based CBCT systems.&#xD.