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arXiv · 2306.16002

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

Jiongtao Zhu·Ting Su·Xin Zhang·Han Cui·Yuhang Tan·Hairong Zheng·Dong Liang·Jinchuan Guo·Yongshuai Ge
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Abstract

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.

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Jiongtao Zhu, Ting Su, Xin Zhang, Han Cui, Yuhang Tan, Hairong Zheng, Dong Liang, Jinchuan Guo, Yongshuai Ge. 2023-06-28. Super resolution dual-layer CBCT imaging with model-guided deep learning. https://arxiv.org/abs/2306.16002

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