Near-isotropic super-resolution CBCT imaging with a dual-layer flat panel detector.
Authors
Affiliations (6)
Affiliations (6)
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, CHINA.
- Research Center for Advanced Detection Materials and Medical Imaging Devices, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences, ., Shenzhen, Guangdong, 518055, CHINA.
- Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, CHINA.
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, P.R.China, Shenzhen, 518055, CHINA.
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, P.R.China, Shenzhen, 518055, CHINA.
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, CHINA.
Abstract
For medical imaging, usually, it is crucial to have high spatial resolution. Studies have demonstrated that novel dual-layer flat panel detectors (FPDs) can acquire extra spatial information to enable super-resolution cone beam CT(CBCT) imaging compared to the conventional single-layer FPDs. The aim of this study is to investigate the feasibility of realizing near-isotropic super-resolution CBCT imaging with a dual-layer flat panel detector.
Approach: To retrieve the near-isotropic super-resolution imaging information, a general mathematical signal model, which includes the relative shift (∆u, ∆v) between the two detector pixel arrays and the gap ∆d between the two detector layers, is established. Numerical simulations are performed to investigate the impact of the relative shift (∆u, ∆v) and the gap ∆d under different scenarios. Afterwards, a RNN-based deep neural network, named as 2D-suRi-Net, is developed to efficiently retrieve the projections having near-isotropic super-resolution imaging information. The real performance of this proposed 2D-suRi-Net approach is validated by a pig leg specimen and an intersecting PTFE cylinder phantom.
Main results: It is found that introducing half pixel shift, i.e., ∆u=∆v=0.5δ del , between the two detector layers is necessary for super-resolution CBCT imaging, particularly when the detector gap ∆d is less than 3 mm. Results demonstrate that the proposed 2D-suRi-Net can effectively retrieve higher spatial resolution information from the acquired low-energy and high-energy projections having lower spatial resolution. On average, quantitatively, the spatial resolution, i.e., the 10% MTF, of the reconstructed CBCT images improves by over 30% and 50% for the top and bottom detector layers, respectively. In addition, the average 10% MTF difference on the axial plane and coronal plane is less than 6%.
Significance: In summary, this study demonstrates the feasibility of near-isotropic super-resolution imaging for dual-layer FPD based CBCT imaging systems.